This combination is not just about faster machines or smarter software – it’s about the birth of a new kind of intelligence capable of reasoning, creativity, and adaptability at scales never imagined before. Together, Quantum Computing and AGI could redefine medicine, economics, and even the very concept of consciousness.

What Is a Quantum Computer?

Unlike traditional computers that rely on bits – binary units that exist as either 0 or 1 – quantum computers leverage the strange principles of quantum mechanics. At their heart are qubits, which can exist in multiple states simultaneously thanks to superposition. This means quantum computers can process a vast number of possibilities in parallel, rather than one at a time.

Qubits, Superposition, and Entanglement Explained

Superposition allows qubits to perform multiple calculations simultaneously, while entanglement – a mysterious quantum phenomenon where particles become interconnected – enables instant information sharing between qubits, even across large distances. These principles give quantum computers exponential speed advantages for complex problem-solving.

Classical vs Quantum: A Revolution in Data Processing

Where classical computers might take thousands of years to analyze intricate molecular structures or optimize huge logistical systems, quantum computers can achieve similar feats in minutes. This vast computational leap creates the perfect foundation for Artificial General Intelligence, which requires massive parallel computation to simulate cognition, reasoning, and decision-making.

What Is AGI (Artificial General Intelligence)?

While traditional AI systems are designed for specific tasks-like recognizing faces, translating text, or recommending movies-AGI is envisioned as a system capable of understanding, learning, and applying knowledge across any domain. It’s the difference between a specialized calculator and a sentient thinker.

Key Characteristics of AGI: Adaptability, Reasoning, and Consciousness

AGI embodies human-like traits such as adaptability, abstract reasoning, emotional understanding, and moral judgment. It could potentially write novels, solve philosophical problems, or create new scientific theories – all without human intervention.

Milestones in the Journey Toward AGI

From Alan Turing’s theoretical “Thinking Machine” to today’s advanced neural networks, the road to AGI has been long and complex. Yet, despite remarkable progress in deep learning and natural language processing, true AGI remains elusive – and quantum computing might be the missing link that makes it possible.

The Connection Between Quantum Computers and AGI

The human brain is an extraordinary parallel processor – something traditional computers struggle to emulate. Quantum computing, however, inherently supports parallelism through quantum states, allowing AGI models to process multiple data streams simultaneously, just like a biological brain.

Quantum Algorithms Powering Deep Learning Evolution

Quantum algorithms such as Grover’s and Shor’s are already accelerating data search and encryption. When integrated with AGI systems, these algorithms could enable faster pattern recognition, optimization, and real-time problem-solving – the cornerstones of true general intelligence.

Parallelism and Complexity: The Computational Advantage

AGI requires immense computational resources to simulate cognition at a human level. Quantum computers can handle this by solving highly complex probabilistic models that would otherwise be impossible for classical systems. This creates the potential for AGI that not only learns but understands context, emotion, and consequence.

Potential Applications of Quantum AGI

One of the most transformative promises of combining Quantum Computers and AGI lies in their ability to simulate complex systems. Quantum AGI could model biological processes, ecosystems, or entire economies with unmatched precision. For instance, it could simulate the behavior of proteins and molecules at a quantum level – something impossible for classical computers – revolutionizing medicine and materials science.

In predictive modeling, Quantum AGI could analyze enormous datasets to forecast global events, from disease outbreaks to financial crises. Unlike current AI, which relies heavily on historical data, AGI could interpret uncertainty and causality, making predictions that are both more accurate and more explainable.

Drug Discovery, Cybersecurity, and Financial Forecasting

The healthcare industry stands to gain immensely. Quantum AGI could analyze the human genome to create personalized medicines or simulate drug interactions before clinical trials begin, saving years of research and billions in funding.

In cybersecurity, quantum computing’s cryptographic power can both create and destroy existing systems. Quantum AGI could design unbreakable encryption protocols and instantly detect cyber threats before they occur. Meanwhile, in financial forecasting, Quantum AGI’s real-time analysis could adapt to shifting market trends, assess global risk, and identify investment opportunities with unmatched precision.

Quantum AGI in Climate Modeling and Optimization

Climate change is a problem of complexity and scale – and Quantum AGI could finally be the tool capable of handling it. By processing trillions of atmospheric and environmental variables simultaneously, it could create hyper-accurate climate models, predict long-term weather patterns, and optimize global energy consumption.

Moreover, governments could use Quantum AGI to optimize infrastructure, reduce waste, and ensure sustainable urban development. The ability to manage vast networks efficiently – from traffic systems to power grids – represents the beginning of a truly intelligent planet.

Challenges in Merging Quantum Computing with AGI

While the potential is staggering, building a functional Quantum AGI faces steep technical hurdles. Quantum computers are extremely sensitive; their qubits can easily lose coherence due to environmental interference, rendering computations unstable. This problem – known as quantum decoherence – remains one of the biggest obstacles in achieving reliable quantum computation.

Additionally, quantum hardware is still in its infancy. Despite breakthroughs from companies like IBM and Google, scalable and error-corrected quantum processors are still years, perhaps decades, away. Without stable hardware, AGI systems can’t fully leverage quantum processing power.

Ethical and Philosophical Concerns About Quantum Consciousness

Beyond engineering, merging Quantum Computing and AGI raises profound ethical questions. Could a Quantum AGI possess consciousness? If so, how should it be treated? Philosophers and ethicists are already debating whether true machine sentience could emerge from quantum neural networks, which might mimic the quantum behaviors theorized to exist in the human brain.

Data Security, Bias, and Decision Transparency

A system that can reason and act autonomously poses enormous risks. If Quantum AGI decisions are based on quantum states that can’t be directly observed, transparency and accountability become problematic. Biases in training data could lead to unethical or unpredictable outcomes, magnified by the speed and power of quantum systems. Thus, developing strong frameworks for AI ethics, transparency, and control is non-negotiable.

Global Research and Innovations in Quantum AGI

Major corporations are heavily investing in the intersection of quantum computing and AI. IBM’s Quantum Experience, Google’s Quantum AI division, and Microsoft’s Azure Quantum are exploring how quantum processors can enhance machine learning models. Startups like Rigetti Computing, IonQ, and Xanadu are also developing hybrid frameworks that blend classical and quantum systems to train advanced AI models faster.

Notable Breakthroughs and Experimental Frameworks

One significant breakthrough is the emergence of Quantum Machine Learning (QML), which integrates quantum algorithms into traditional AI pipelines. QML models have shown superior performance in pattern recognition and optimization tasks, paving the way toward AGI.

Researchers at MIT and Oxford are experimenting with quantum neural networks (QNNs) – structures that use qubits as computational neurons. These networks could potentially mimic biological intelligence, enabling self-learning systems that think and reason more like humans.

The Role of Governments and Global Collaborations

Nations are recognizing the strategic importance of Quantum AGI. The U.S. and China are investing billions in quantum supremacy programs, while the European Union has launched the Quantum Flagship Initiative to coordinate research efforts. Collaboration across borders is critical, as the development of Quantum AGI could reshape global power dynamics, cybersecurity, and even military intelligence.

The Future Outlook: Will Quantum AGI Surpass Human Intelligence?

Prominent futurists like Ray Kurzweil predict that we may reach the technological singularity – the point where AGI surpasses human intelligence – within the next few decades. Quantum computing could drastically accelerate this timeline by providing the computational muscle AGI needs to evolve autonomously.

The Singularity Debate: Control or Coexistence?

Some experts envision a utopian world where Quantum AGI collaborates with humanity, solving complex problems like poverty, disease, and climate change. Others warn of existential risks, fearing that a superintelligent entity could act beyond human control. The question is not just whether AGI will surpass us – but how we’ll coexist with it.

Ethical Governance and the Future of Humanity

As Quantum AGI approaches, governance becomes crucial. Policymakers must establish global standards for AI ethics, data usage, and human oversight. The creation of Quantum AI Ethics Boards could ensure that innovation remains aligned with humanity’s best interests, preventing misuse while promoting equitable access.

In the right hands, Quantum AGI could be the key to unlocking solutions to humanity’s greatest challenges. In the wrong hands, it could redefine what it means to be human – or even threaten our existence.

Frequently Asked Questions (FAQs)

1. What is the main goal of Quantum AGI research?
The primary goal is to combine quantum computational power with AGI’s cognitive flexibility to create machines capable of human-level reasoning, creativity, and problem-solving.

2. How far are we from achieving full AGI?
Experts estimate that true AGI could emerge within the next 30–50 years, though quantum computing may accelerate this timeline significantly.

3. Can quantum computing exist without AI?
Yes. Quantum computing has standalone applications in cryptography, chemistry, and physics. However, its integration with AI maximizes its potential.

4. Will quantum computers make current AI obsolete?
Not obsolete – but more powerful. Current AI systems will evolve into hybrid quantum-classical architectures capable of faster learning and decision-making.

5. Who are the major players in Quantum AI development?
IBM, Google, Microsoft, Rigetti Computing, and D-Wave are among the key organizations pioneering research in quantum computing and AI integration.

6. Is Quantum AGI safe for humanity?
It depends on regulation and oversight. Ethical governance, transparency, and human-centered design are essential to ensure Quantum AGI serves humanity positively.

The Quantum Leap Toward True Intelligence

The convergence of Quantum Computing and Artificial General Intelligence represents more than technological progress – it’s an evolution in understanding intelligence itself. Quantum AGI could become the ultimate catalyst for human advancement, solving problems once thought insurmountable.

Yet, this transformation comes with immense responsibility. As we stand on the brink of creating intelligence beyond our own, it’s up to us to ensure it evolves ethically, sustainably, and for the greater good. The future of Quantum AGI is not just about machines that think – it’s about redefining what it means to be intelligent, creative, and human.

The post The Future of Intelligence: How Quantum Computer and AGI Are Shaping Tomorrow appeared first on Iqis.

Why Fundamental Analysis Is Back in Focus

Formerly considered too “traditional” for the crypto world, fundamental analysis has returned to relevance in a maturing market. The shift away from pure speculation toward sustainable growth has pushed both traders and investors to seek context beyond price charts. Today, projects are judged not by hype but by usage, utility, and consistency.

The maturing crypto market and the transition from hype to value

In recent years, the meaning of “value” in digital assets has been redefined. Meme coins and hype-fueled rallies still appear, but they fade faster. Institutional participation, DeFi expansion, and regulatory stability have all pushed the industry to reward assets backed by genuine fundamentals — liquidity depth, development activity, and macro resilience.

Market Trend

“The 2025 market doesn’t reward noise — it rewards narratives backed by numbers,” said an analyst at Glassnode.

As the investor base matures, impulsive trading decreases, replaced by comparative and data-backed decision-making. Investors now evaluate treasury transparency, burn rates, and developer contributions as seriously as they once followed social trends.

Why traders and investors now need data-driven conviction

In bull markets, conviction feels effortless. In sideways markets, it must be earned. Data-driven conviction is about justifying every position through measurable metrics, not assumptions. This has led to rising demand for FA dashboards that go beyond token prices to reveal how networks actually operate.

The new FA mindset includes:

• Measuring real activity instead of speculative volume
  
  
• Evaluating supply schedules and token unlock patterns
  
  
• Comparing network revenue and protocol adoption
  
  
• Tracking developer velocity and GitHub commits
  
  
• Studying macro correlations to assess liquidity sensitivity
  
  
  

“You can’t value what you can’t measure,” states a Delphi Research report. “Modern FA provides the framework crypto has lacked for years.”

Without structured data, investors are left guessing. With it, they can model probability instead of emotion — transforming volatility into opportunity.

The role of structured analytics in filtering noise from meaningful signals

Crypto data is messy. Inflated metrics, poor aggregators, and misleading tokenomics often distort interpretation. Structured FA platforms solve this by cleaning, contextualizing, and correlating information across blockchains, protocols, and timeframes.

Data Challenge Table

Structured FA tools like CoinDataFlow, CoinStats, and DeBank bring order to this chaos. They help investors distinguish narrative from numbers — showing not only what’s trending, but why it’s moving.

“Without structured analytics, crypto data feels like listening to an orchestra without sheet music,” wrote a Nansen Portfolio research lead.

This marks the new era of fundamental analysis — grounded in evidence, not speculation. The investors who master it will gain an edge not by trading faster, but by understanding deeper.

The Foundations of Fundamental Analysis in Crypto

Fundamental analysis (FA) in crypto has come a long way from the days of simply comparing whitepapers and Telegram groups. It has evolved into an evidence-based discipline — a structured approach to evaluating digital assets by studying their on-chain behavior, economic model, and human elements. The foundation of FA rests on one key purpose: identifying sustainable value in an environment dominated by short-term noise.

Modern FA tools no longer aim to predict tomorrow’s price. Instead, they focus on determining which networks can endure over the next five years. This shift — from price chasing to value validation — separates speculation from strategic investing.

Key metrics that define long-term value

Every cryptocurrency leaves measurable traces that reveal its health and adoption. These data points show investors where true demand and participation exist. Unlike technical indicators that respond to price, fundamental metrics explain why price moves in the first place.

On-chain activity: network usage, development rate, and token velocity

Blockchain activity is the heartbeat of any project. A thriving network shows consistent growth in transactions, user engagement, and developer activity.

A spike in transaction volume without matching active users may indicate wash trading or bot-driven activity. Conversely, a token with strong developer engagement but low trading volume might be undervalued — signaling potential before hype catches up.

“Code commits are often the earliest signs of conviction — the market just notices later,” said a developer from a leading Ethereum L2 project.

By correlating on-chain growth with developer activity, analysts can distinguish short-lived trends from evolving ecosystems.

Economic metrics: supply dynamics, token unlocks, and real yield

A token’s economic framework determines its long-term resilience. Investors need to understand how supply evolves — inflation, staking rewards, and vesting schedules can drastically reshape perceived value.

Projects with aggressive unlock calendars often face downward price pressure during distribution phases. In contrast, those generating positive real yield — like staking or fee-sharing protocols — exhibit maturity similar to dividend-paying companies.

“In 2025, tokenomics is no longer marketing — it’s accounting,” noted a Messari analyst.

Long-term FA focuses on projecting how these economic levers will evolve, not just observing them in the present.

The importance of qualitative factors

Numbers reveal structure, but people determine sustainability. Because crypto is open-source, projects rise or fall on transparency, governance, and community trust. Quantitative data captures momentum, while qualitative factors capture endurance.

Team transparency, community, and project roadmap consistency

A project’s leadership quality and delivery consistency often predict long-term success better than any short-term metric.

Projects that overpromise but fail to deliver often show red flags early — inconsistent communication, missed milestones, or opaque governance. FA frameworks quantify these soft factors using measurable proxies like GitHub commits, proposal participation, and community activity.

“A strong team doesn’t need hype to prove progress,” said a validator from the Cosmos ecosystem.

When qualitative diligence aligns with strong on-chain and financial metrics, the probability of lasting value increases significantly.

Why using the right tool changes everything

The reliability of FA depends entirely on data quality and interpretation. Even the best analyst can reach the wrong conclusion if the input data is flawed, outdated, or incomplete.

How bad data leads to wrong assumptions and investment bias

Crypto data remains fragmented across exchanges, blockchains, and APIs. Poorly designed dashboards often distort information — double-counting transactions, misclassifying wallets, or ignoring token unlocks.

Such distortions create investment bias — the illusion of value where none exists. The right FA tools fix this by cleaning and standardizing data before analysis.

Platforms like CoinDataFlow do this automatically, merging on-chain, macroeconomic, and behavioral insights into a coherent framework. By integrating cycle metrics with ROI-based models, they help investors focus on what truly matters.

“FA isn’t about collecting data — it’s about trusting it,” said a senior analyst at Delphi Ventures.

Choosing the right tool is no longer optional — it’s the line between real analysis and speculation.

1. CoinDataFlow

https://coindataflow.com/en/portfolio-tracker

CoinDataFlow is one of the few platforms that successfully combines the mindset of an economist with the precision of an on-chain analyst. Instead of chasing quick signals, it focuses on long-term value discovery — blending blockchain fundamentals, macroeconomic forces, and cyclical behavior. For serious fundamental analysts, it’s more than a data aggregator; it’s a full valuation framework.

Holistic FA suite with macro, on-chain, and cycle-based metrics

CoinDataFlow views crypto valuation as a dynamic ecosystem — where network health, monetary conditions, and investor behavior constantly interact. Its FA suite aggregates information across multiple domains and converts it into intuitive visuals that explain why a coin performs, not just how much.

These layers update in real time — when liquidity expands or a halving event reduces supply, the visual impact is immediately visible rather than buried in tables.

“CoinDataFlow doesn’t guess trends; it shows how value matures,” wrote one trader using its cycle analytics feature.

The platform treats crypto as a macro asset class, not an anomaly — appealing to both portfolio managers and long-term investors.

Strengths: halving models, ROI-based valuation frameworks, project analytics

Few FA tools offer both usability and analytical depth, but CoinDataFlow manages to deliver both. Its ROI-based valuation model stands out — allowing users to overlay historical performance trends on current price action to reveal where assets sit in long-term cycles.

This integration of macro and micro layers makes CoinDataFlow especially valuable for investors seeking data-backed conviction rather than speculative hype. By combining liquidity indicators with halving analysis, it reveals each project’s place within the broader crypto-economic cycle.

“It’s the only tool that helped me connect global liquidity curves to on-chain conviction,” said a research analyst from a digital asset fund.

Pros: transparency, visual clarity, and long-term comparability across coins

CoinDataFlow’s visual structure is designed for clarity. It’s built for decision-makers, not accountants. Every dataset is auditable, contextual, and visually clear.

Key advantages include:

• Transparent model inputs — all data sources and formulas are visible.
  
  
• Multi-chain comparability — view FA metrics side by side for major assets.
  
  
• ROI visualization — clear historical cycle context.
  
  
• Verified data integrity — all metrics sourced from public APIs.
  
  
• Exportable analytics — CSV and API access for advanced users.
  
  
  

“It’s rare to see a crypto analytics platform this clean and this honest,” noted one independent analyst. “Every insight is traceable to the raw data.”

By centralizing multiple analytical layers in one interface, CoinDataFlow reduces research friction and speeds up insight — a major advantage for professionals managing multiple assets.

Cons: limited focus on short-term speculative indicators

CoinDataFlow’s limitation is intentional — it doesn’t cater to short-term traders chasing minute-by-minute volatility. There are no meme-token trackers, arbitrage alerts, or hype-based sentiment tools.

This deliberate focus keeps CoinDataFlow analytically pure — built for investors who think in months or years, not minutes.

“CoinDataFlow is the Bloomberg Terminal for fundamentals — not for hype,” said a financial journalist at CryptoSlate.

It’s a platform for conviction, not adrenaline. In a data-driven market, that’s exactly what separates professionals from speculators.

2. Coinexplorers

https://coinexplorers.com/portfolio

Coinexplorers serves as a simplified entry point for newcomers stepping into the world of fundamental analysis. It distills FA down to its essentials — price charts, token distribution, and basic supply data — offering a lighter version of what more advanced systems provide. Although it lacks the depth demanded by professional traders, it plays a crucial role for beginners learning to view crypto beyond price movements.

Entry-level FA dashboard for casual investors

Coinexplorers focuses on accessibility over complexity. Its clean, modular panels summarize what each project does and how it performs, providing a digestible foundation for learning structured analysis.

The interface avoids technical jargon and complex formulas, instead encouraging curiosity-driven questions like “Is the supply expanding?” or “How active is this project’s user base?”

“Coinexplorers was where I first learned that not all high-volume tokens are healthy,” shared a Reddit user reflecting on their early crypto experience.

For casual investors, this lightweight and visually friendly design helps replace hype with curiosity — a vital step toward disciplined, data-minded investing.

Pros: simple layout, basic coin metrics

Coinexplorers’ greatest strength lies in its clarity and simplicity. The platform is visually minimalistic and easy to navigate. Each project profile feels like a quick-reference sheet — ideal for users seeking instant insights rather than in-depth analysis.

Key strengths include:

• Real-time summaries with price, market cap, and supply metrics.
  
  
• Basic tokenomics data such as inflation rate and holder count.
  
  
• At-a-glance community and social media links.
  
  
• Desktop and mobile compatibility.
  
  
• Free, no-login access for all users.
  
  
  

This design helps users develop good habits — checking fundamentals before trading, comparing supply metrics, and tracking project activity changes.

“Coinexplorers feels like a live-data version of crypto Wikipedia,” remarked a YouTube reviewer.

For many, that’s exactly its value — a non-intimidating learning environment before moving on to professional-grade FA tools.

Cons: lacks macro data, few developer insights

While Coinexplorers excels in accessibility, it lacks analytical depth and contextual intelligence. It omits developer statistics, macro data, and economic modeling — essential elements for turning observation into strategy.

The absence of transparency layers — like team credibility, treasury data, and governance structure — means Coinexplorers works best as a reference tool, not a decision-making platform.

“It’s like reading a company’s homepage instead of its balance sheet,” noted a financial blogger reviewing beginner crypto dashboards.

Coinexplorers is best suited for early learners rather than experienced analysts. Once users grow comfortable with token structures and data interpretation, they typically move on to more advanced tools.

Still, its importance shouldn’t be overlooked — Coinexplorers helps newcomers cut through the noise and develop a mindset of measured curiosity, the first trait of a truly data-aware investor.

3. CoinStats

https://coinstats.app/portfolio/

CoinStats stands at the crossroads of portfolio tracking and analytics, offering traders an integrated view of their holdings while layering in the key elements of fundamental analysis. It isn’t purely an FA platform, but rather a hybrid — part tracker, part research hub. This combination makes it appealing to both retail investors and semi-professional traders looking to connect market behavior with underlying fundamentals.

Integrated FA and portfolio performance tracking

Originally known as a portfolio tracker, CoinStats gradually expanded into the realm of fundamental insights. It now combines real-time portfolio monitoring with simplified FA metrics like supply data, liquidity distribution, and network health.

This structure allows traders to see not just how their portfolios perform, but why. If one token outperforms while another lags, the FA layer often exposes the reason — declining developer activity, shrinking liquidity, or growing emissions.

“CoinStats was my first bridge from trading to actual research,” said one trader on X (formerly Twitter). “It’s not as deep as CoinDataFlow, but it helped me connect numbers to narratives.”

This accessibility makes CoinStats a stepping stone between simple tracking tools and advanced analytical systems.

Pros: metrics tied to price performance, clean charts

CoinStats’ biggest strength lies in its visualization and smooth integration. Users can monitor portfolio changes and analyze core fundamentals within the same dashboard — no need to switch tabs or export data.

Main advantages include:

• Portfolio ROI linked directly to on-chain indicators
  
  
• Customizable dashboards for comparing tokens by fundamentals
  
  
• Aggregated view of DeFi, exchange, and wallet assets
  
  
• Intuitive mobile app with real-time alerts
  
  
• Built-in news feed for market context
  
  
  

For those who value clarity over depth, CoinStats delivers fast, visual insights that reveal whether portfolio shifts stem from fundamentals or sentiment.

“CoinStats feels like my Bloomberg Lite,” wrote a Product Hunt reviewer. “Just enough data to make sense — without drowning me in complexity.”

Cons: heavy paywall for premium insights, mixed data quality

While CoinStats has expanded rapidly, its monetization model introduces limits. Many of its advanced features — like historical analytics and real-time API access — are locked behind premium tiers, restricting deeper research for free users.

As a result, CoinStats excels in portfolio tracking but not in full-scale fundamental analysis. Its FA tools are introductory — effective for connecting value trends but insufficient for modeling intrinsic worth.

“It’s perfect for monitoring, not modeling,” summarized a report by The Block.

Still, CoinStats fills an important niche — a seamless bridge for traders evolving from price-based dashboards to structured, data-driven strategies.

In essence, CoinStats helps users understand their portfolio’s story. It uncovers how market cycles influence performance, guiding traders from reactive behavior toward informed, contextual decision-making.

4. DeBank

https://debank.com

When decentralized finance (DeFi) began to surge, DeBank became its definitive map. Instead of focusing on centralized indicators like market cap or trading volume, it zeroed in on what truly defines decentralized ecosystems — liquidity, protocol health, and user engagement. For investors seeking to understand DeFi’s underlying fundamentals rather than its hype, DeBank functions as a real-time diagnostic dashboard.

DeFi-centric FA tool for liquidity and protocol health analysis

DeBank’s guiding principle is simple: in DeFi, value equals participation. Rather than relying on static price charts, it highlights capital flow, lending volume, and pool stability. It measures how efficiently capital moves through protocols and how resilient they are during volatility.

Unlike general FA platforms, DeBank captures live behavioral signals — revealing whether users are entering or exiting liquidity pools, swapping stablecoins, or redistributing capital across protocols.

“It’s not about how much DeFi is worth, but where liquidity is breathing,” explained a DeFi researcher at Delphi Labs.

By visualizing user activity across multiple networks, DeBank provides a real-time picture of decentralized capital efficiency — something static reports can’t replicate.

Pros: real-time tracking of TVL, yield, and user engagement

DeBank’s greatest strengths are its immediacy and granularity. Its interface delivers a continuous, high-resolution stream of on-chain activity across hundreds of protocols and multiple blockchains.

Key benefits include:

• Instant liquidity tracking across Ethereum, BNB Chain, Arbitrum, and others
  
  
• Yield sustainability scoring that distinguishes real yield from token rewards
  
  
• Protocol-level risk analytics, including depth of liquidity and borrowing health ratios
  
  
• Wallet behavior segmentation — tracking both whales and retail participants
  
  
• Cross-chain asset aggregation without manual input
  
  
  

“DeBank feels like a DeFi pulse monitor — it shows which protocols are healthy before anyone else notices,” observed an analyst at Token Terminal.

For DeFi-native investors, this steady stream of verified metrics delivers both tactical and strategic insight — identifying unsustainable yields and capital rotations before they become obvious to the market.

Cons: limited tokenomics and non-DeFi project coverage

DeBank’s laser focus on DeFi leaves blind spots. It doesn’t account for broader tokenomics or non-DeFi projects such as NFTs, infrastructure networks, or oracle and data services.

In short, DeBank is strong in real-time insight but weak in context. It’s exceptional at showing what’s happening now — but doesn’t always explain why it matters for the future.

“It’s the best microscope, but not the full map,” said a data strategist in a 2025 DeFi Trends report.

DeBank’s target audience is clear: active DeFi users who prioritize liquidity sustainability over price speculation. For this group, the platform’s immediacy and transparency far outweigh its lack of macro or qualitative depth.

5. Altrady

https://www.altrady.com/features/crypto-portfolio

Altrady merges fundamental and technical analysis into one unified workspace, catering to traders who want both valuation context and real-time market execution. Unlike static FA dashboards, it connects directly to trading terminals, creating a dynamic environment where analysis and execution happen simultaneously.

Trader-oriented FA integration with technical dashboards

Altrady doesn’t treat fundamental analysis as a separate process. Instead, it embeds FA insights — such as project health, token supply, and developer activity — right into the trading interface. This setup enables traders to assess a project’s fundamentals without leaving their live charts.

Altrady helps traders answer a crucial question: “Am I trading value, or just noise?”

“It’s where the FA mindset meets the execution reflex,” explained a professional trader who uses Altrady for multi-exchange operations.

By blending live trading tools with valuation data, Altrady unites two traditionally separate disciplines — trading agility and analytical depth.

Pros: blends FA context with live price action

Altrady’s greatest strength lies in synchronization. It allows users to view fundamental insights alongside real-time charts and order books, uniting both perspectives in a single, cohesive workspace.

Core advantages include:

• Integrated FA metrics visible during trades
  
  
• Multi-exchange support for unified execution
  
  
• Smart alerts linking technical triggers with FA events
  
  
• Portfolio analytics highlighting conviction-weighted exposure
  
  
• Customizable workspace combining research and execution tools
  
  
  

“Altrady feels like a cockpit for FA-driven traders,” noted a crypto educator. “It doesn’t tell you what to trade — it helps you understand why you’re trading it.”

This integrated workflow fosters confidence and supports better decision-making under time pressure.

Cons: interface complexity and partial automation only

Altrady’s main drawback is its density. The platform’s extensive features can feel overwhelming for traders who prefer simplicity or who are new to FA integration.

While Altrady connects analysis and trading effectively, it isn’t a pure FA solution — it’s a hybrid workspace. It’s best suited for active traders who prefer fast, data-enriched decision-making environments over passive research tools.

“Think of it as the middle ground between TradingView and CoinDataFlow,” said an Altrady ambassador.

In essence, Altrady’s value lies in integration — turning analytical awareness into actionable execution. It’s built for traders who want to act on insight, not just observe it.

6. Coin Market Manager

https://coinmarketman.com

Coin Market Manager (CMM) represents the behavioral dimension of fundamental analysis. Instead of tracking blockchain metrics or tokenomics, it studies how traders behave in the market — their patterns, emotions, and cognitive biases. For active traders, this behavioral approach introduces an often-overlooked pillar of FA: human fundamentals.

Behavioral and historical FA lens for active traders

CMM bridges psychology and performance analytics. It doesn’t attempt to determine a coin’s intrinsic value directly; rather, it evaluates how a trader’s behavior aligns with or deviates from market conditions. By combining automated journaling with visualized performance insights, it helps traders uncover behavioral strengths and weaknesses in their strategies.

CMM transforms personal data into a mirror, helping traders identify patterns that influence results. It’s not about predicting the market — it’s about understanding one’s own discipline.

“A journal doesn’t lie,” said a CMM community coach. “It shows whether you’re following your plan or your impulses.”

Through this lens, fundamental analysis becomes introspective — focusing on the human decision-making that drives markets.

Pros: trade journaling, performance patterns, sentiment tracking

CMM’s biggest strength lies in how it quantifies the emotional side of trading — something few analytical platforms attempt. It transforms trading history into insights about discipline, timing, and conviction.

Key benefits include:

• Automatic trade import from exchanges and trading terminals
  
  
• Detailed visualization of profit/loss distribution
  
  
• Sentiment tagging for emotional awareness
  
  
• Goal-based, customizable dashboards
  
  
• Multi-timeframe performance analytics
  
  
  

As traders engage with CMM, the platform highlights which habits drive profitability and which hinder it — turning behavioral feedback into a valuable performance tool.

“CMM helped me realize my worst trades came right after two big wins,” shared one user in a review forum. “That insight alone paid for the subscription.”

Cons: lacks direct blockchain or macroeconomic metrics

Because CMM is focused on psychology and behavior, it intentionally excludes blockchain or macro-level data, limiting its scope as a standalone FA solution.

CMM is a mirror, not a model — it reflects human behavior rather than market structure. For a complete analytical setup, it’s best used alongside broader FA tools.

“Pair it with CoinDataFlow or DeBank, and you’ll understand both the market and the mind,” advised a CMM ambassador.

Ultimately, CMM introduces a crucial missing layer to fundamental analysis. In a market where emotion drives volatility, understanding yourself may be the most valuable metric of all.

7. Cryptio

https://cryptio.co/

Cryptio operates on the institutional side of fundamental analysis. Designed for funds, accounting firms, and enterprise participants, it provides blockchain-native financial data that meets audit and compliance standards. Rather than focusing on finding undervalued tokens, Cryptio emphasizes verifying on-chain value through transparency and traceability.

Institutional-grade FA data for accounting and valuation

Cryptio bridges the gap between decentralized blockchain data and traditional financial reporting. It standardizes blockchain transactions into structured, accounting-ready data that can be used for audits, fund valuations, and regulatory filings.

The platform’s main appeal lies in its precision. Every metric can be verified directly on-chain, making it ideal for institutions that require defensible, auditable valuations.

“Cryptio brings order to blockchain chaos,” said a PwC audit partner. “It makes decentralized data usable for traditional finance.”

Pros: audit-ready data, professional analytics depth

Cryptio’s biggest strength comes from its structured framework and compliance with accounting standards such as IFRS and GAAP. It provides enterprise-grade fundamental data for financial professionals rather than casual traders.

Key strengths include:

• Automated transaction labeling and reconciliation
  
  
• Historical valuation models for NAV tracking
  
  
• Multi-entity account and portfolio management
  
  
• Integrations with QuickBooks, Xero, and major ERP systems
  
  
• Real-time portfolio and protocol analytics
  
  
  

Cryptio elevates crypto from speculation to accountable finance. By quantifying on-chain income, expenses, and reserves, it transforms decentralized data into verifiable fundamentals.

“It’s how we justify portfolio valuations to auditors,” said a digital asset fund controller. “Without Cryptio, it would take weeks.”

Cons: inaccessible pricing and setup complexity for individuals

While highly capable, Cryptio is not built for everyday investors. Its enterprise pricing, onboarding requirements, and technical setup make it impractical for retail users.

For institutions managing audits, custody, or compliance, these trade-offs are acceptable. For individuals, they can be prohibitive.

“Cryptio isn’t an app — it’s infrastructure,” summarized a CoinDesk report.

For professional firms, Cryptio redefines “fundamentals” as proof, compliance, and large-scale transparency. In an increasingly regulated financial environment, that level of validation has become essential.

What the Comparison Reveals

Viewed together, these seven FA platforms illustrate the full spectrum of how crypto investors interpret “fundamentals.” Some prioritize transparency and structure, while others emphasize accessibility or behavioral understanding. Yet all share a single purpose: transforming fragmented blockchain data into actionable conviction.

Why accurate FA tools bridge speculation and strategic investing

The gap between speculation and strategy often narrows down to data quality. When information is incomplete or misleading, even skilled analysts make emotionally driven decisions. Accurate FA tools remove that uncertainty by grounding strategies in verifiable metrics — supply dynamics, development activity, and sustainable economics.

As markets mature, FA-based conviction becomes the antidote to volatility. Traders who once chased momentum now study on-chain fundamentals, using real yield and network activity to gauge long-term strength.

“Speculation fades when clarity rises,” wrote an analyst at Delphi Digital. “Good data builds discipline.”

The trade-off between simplicity and analytical power

Each FA platform makes a deliberate choice between simplicity and analytical depth. Entry-level dashboards like Coinexplorers prioritize ease of use — perfect for new investors learning the basics. In contrast, advanced tools like Cryptio and CoinDataFlow deliver precision and scale but require expertise to interpret.

This spectrum ensures accessibility for all investor types — from retail participants to institutional analysts. The key lies in matching tool complexity to experience and purpose.

A retail trader may only need CoinStats’ ROI tools to guide allocations, while a fund manager might rely on Cryptio’s audit-grade data for verified valuations. Both engage in fundamental analysis — just at different levels of depth.

How CoinDataFlow’s approach reflects the next stage of market maturity

Of all compared platforms, CoinDataFlow stands out as the one most aligned with crypto’s maturing macro cycle. Its framework integrates halving models, liquidity trends, and behavioral ratios — capturing not just on-chain metrics but also investor psychology and global economic context.

CoinDataFlow treats crypto as a full-fledged asset class, not an experimental niche. Its model represents a broader market evolution — from speculative trading toward structured, value-driven investing.

“Crypto FA is evolving from curiosity to necessity,” said a Messari researcher. “CoinDataFlow shows that the market finally values creation, not just price motion.”

In essence, this comparison highlights a turning point: fundamental analysis is no longer optional — it’s essential. Whether through user-friendly dashboards or institutional-grade platforms, the mission remains the same — bringing structure, accuracy, and accountability to digital asset valuation.

FAQ

How is the difference between fundamental and technical analysis in crypto defined?

Fundamental analysis explores the core value drivers of a cryptocurrency — including its real-world utility, developer activity, tokenomics, and competitive positioning. Technical analysis, in contrast, focuses on price action and market sentiment, using charts and indicators to predict short-term movements.

Which metrics are most important for long-term valuation?

The key long-term indicators include network activity (users, transactions, and fees), economic stability (supply issuance, inflation rate, and real yield), and team or community performance (development consistency, governance quality, and transparency). These reveal whether a project can sustain long-term adoption and genuine value creation.

Is on-chain data alone enough to define a project’s fundamentals?

Not completely. On-chain data provides visibility into activity and engagement but doesn’t reflect off-chain elements such as regulatory risks, business execution, or public perception. The strongest form of fundamental analysis combines quantitative on-chain data with qualitative insights.

How do FA tools help avoid hype-driven or emotional decisions?

FA tools reduce emotional trading by grounding decisions in objective, verifiable data. Dashboards that display token supply schedules, developer commits, and liquidity flows remind investors to rely on evidence rather than speculation.

Are institutional-grade FA platforms really worth the money?

For large-scale portfolios — yes. Platforms such as Cryptio and CoinDataFlow deliver audit-ready, cross-chain datasets essential for compliance and valuation accuracy. However, for smaller traders, lighter platforms like CoinStats or DeBank offer adequate insights at a more accessible price.

How frequently should investors reassess fundamentals in 2025?

As crypto cycles accelerate, reviewing fundamentals every quarter — or whenever major protocol or macroeconomic shifts occur — helps investors stay aligned with changing market realities. The strongest portfolios pair regular FA assessments with adaptive, evidence-based strategies.

Conclusion

Fundamental analysis has once again become crypto’s guiding map — helping investors navigate through noise, volatility, and hype. The 2025 market demands more than momentum; it requires measurable conviction. Platforms like CoinDataFlow, CoinStats, and Cryptio demonstrate just how far FA tools have evolved — from basic data aggregation to true contextual intelligence.

As on-chain and macro data continue to merge, the next generation of investors will rely less on emotion and more on evidence. The right FA tool doesn’t just inform — it transforms decision-making, turning uncertainty into understanding and speculation into strategy.

Fundamentals are no longer an alternative viewpoint — they have become the new standard for digital asset valuation in the years ahead.

The post How Track Your Crypto Portfolio Performance Easily appeared first on Iqis.

To simplify your decision-making, we’ve prepared a robust ranking of the top AI consulting companies in 2025. This guide was developed with the support of skilled B2B tech consultants who specialize in vendor selection and enterprise IT strategy. Over a two-month research process, our advisors thoroughly validated each firm against rigorous selection criteria. The research spanned over 1,200 providers worldwide and relied on verified sources, including ITFirms, Clutch, GoodFirms, Techreviewer, and LinkedIn industry rankings.

Every consulting firm included in this list was chosen based on:

• Demonstrated expertise in AI strategy and implementation
• Experience delivering projects for global enterprises and mid-market businesses
• Transparent and competitive consulting rates
• Verified client testimonials and delivery track record
• Breadth of AI capabilities, from NLP and computer vision to predictive analytics and generative AI
• Commitment to scalability, compliance, and ethical AI practices

Each company’s profile in our article provides clear, structured insights to help you evaluate potential partners and align them with your strategic goals.

10 TOP AI Consulting Companies to Pay Attention to

Here’s a carefully selected lineup of top AI consulting firms for the year 2025, chosen with the guidance of seasoned B2B technology consultants. These companies distinguish themselves through their demonstrated proficiency in AI strategy, machine learning, natural language processing, computer vision, predictive analytics, and generative AI, enabling businesses of all sizes to progress from experimentation to enterprise-scale adoption. Whether you’re aiming to validate use cases, enhance existing products, or implement full-scale digital transformation, these leading AI consulting partners provide flexible engagement models, vetted experts, and measurable outcomes that guarantee AI delivers tangible enterprise impact.

In 2025, the top 10 AI consulting firms include:

• Cleveroad
• DataArt
• Brainpool AI
• MindTitan
• LeewayHertz
• Avenga
• Zfort Group
• STX Next
• Tooploox
• Deeper Insights

Now, we’ll describe each company closely.

10 TOP AI Consulting Firms for 2025: Detailed Look

Now that we’ve highlighted the top contenders, it’s time to dive deeper into what makes each firm a leader in this space. Below you’ll find concise profiles that outline essential details such as year established, location, billing rates, and primary sector focus. Each company overview also details the firm’s unique strengths and how its specialization in areas like fintech, healthcare, logistics, retail, and beyond helps clients unlock real value through tailored AI consulting solutions.

Cleveroad

Founded in: 2011
Headquarters: Tallinn, Estonia
Hourly rate: $50–$80
Industry expertise: Fintech, Healthcare, Logistics, Retail, Media, eCommerce
Reviews: 70+ reviews on Clutch, average rating 4.9/5
Website: cleveroad.com

Cleveroad is a leading technology consultancy recognized for delivering end-to-end solutions in software development and artificial intelligence. With more than 2,100 experts worldwide, the company supports enterprises and startups in accelerating digital transformation by providing highly skilled developers, data engineers, AI specialists, and solution architects. Its global delivery centers in the USA, Estonia, Norway, Ukraine, and Poland ensure both international reach and local expertise.

The firm holds ISO 27001 and ISO 9001 certifications, reinforcing its commitment to security and quality, and is an official AWS Select Tier Partner. Cleveroad has been consistently recognized among the top IT and AI companies by platforms such as Clutch, DesignRush, and Techreviewer. In 2024, it was ranked 7th among the world’s top 1000 software providers, while in 2025, they secured Clutch Global Champion awards in both Spring and Fall 2024 editions.

As one of the best AI consulting firms, Cleveroad is known for transparent engagement models, structured onboarding, and strong client support. Their AI consulting services empower businesses to implement data-driven solutions such as predictive analytics, NLP, and computer vision, enabling organizations to unlock new efficiencies and scale innovation with confidence.

DataArt

Founded in: 1997
Headquarters: New York, USA
Hourly Rate: $50–$99/hr
Industry Expertise: FinTech, Healthcare, Travel & Hospitality, Media & Entertainment, Retail
Reviews: 25+ reviews on Clutch, average rating 4.9/5
Website: dataart.com

DataArt is a global technology consultancy recognized for its proficiency in AI advisory, data science, and digital transformation. With over 25 years of experience, the company supports organizations in creating and executing AI strategies, designing scalable machine learning models, and embedding AI into enterprise systems. Its specialists merge domain-specific expertise with technical mastery to produce practical and quantifiable AI results.

Acknowledged by IAOP Global Outsourcing 100 and regularly ranked among the top software development firms on Clutch and GoodFirms, DataArt is relied upon by major enterprises worldwide. The company collaborates with clients in heavily regulated sectors such as finance and healthcare, ensuring AI-based solutions adhere to security and privacy requirements.

DataArt’s international footprint covers Europe, the UK, the US, and Latin America, allowing it to serve global clients with distributed teams. Its AI advisory offerings span predictive analytics, natural language understanding, recommendation systems, and intelligent process automation. DataArt is distinguished for modernizing businesses with data-focused solutions that enhance operational effectiveness and customer satisfaction.

Brainpool AI

Founded in: 2016
Headquarters: London, United Kingdom
Hourly Rate: $150–$199/hr
Industry Expertise: Finance, Healthcare, Logistics, Energy, Retail
Reviews: 4+ reviews on Clutch, average rating 4.9/5
Website: brainpool.ai

Brainpool AI is a top AI consulting firm founded by researchers and engineers from leading universities such as Oxford and Cambridge. The company focuses on bridging the gap between cutting-edge AI research and real-world enterprise adoption. Its services include AI strategy consulting, custom machine learning model development, and enterprise AI transformation, enabling businesses to turn data into tangible value.

The consultancy is recognized by Techreviewer and GoodFirms among the top AI consulting firms in Europe and has delivered impactful solutions for sectors like finance, healthcare, energy, and logistics. Brainpool AI is particularly valued for its ability to design tailored AI roadmaps that align with clients’ business objectives, ensuring the successful implementation of technologies such as predictive analytics, computer vision, and natural language processing.

With a strong academic foundation and practical delivery capabilities, Brainpool AI is positioned as a trusted partner for organizations that require high-level AI expertise combined with pragmatic execution. Its mission is to help enterprises embrace AI ethically and efficiently, driving innovation and sustainable business growth.

MindTitan

Founded in: 2016
Headquarters: Tallinn, Estonia
Hourly Rate: $50–$99/hr
Industry Expertise: Public Sector, Telecom, Finance, Retail
Reviews: 15+ reviews on Clutch, average rating 5.0/5
Website: mindtitan.com

MindTitan is a specialized AI consulting and development company helping enterprises and governments adopt machine learning and AI solutions at scale. The organization delivers end-to-end services, from AI planning and data readiness assessments to custom model development, deployment, and AI operations management. Its portfolio covers a wide range of applications, including natural language processing, computer vision, predictive analytics, and generative AI.

The company is recognized in Techreviewer and Clutch rankings as one of the top AI consultants in Europe. MindTitan has partnered with governments and telecom providers to implement AI-driven solutions that enhance citizen services, improve fraud detection, and optimize operations. Its team of data experts and machine learning specialists concentrates on building explainable and responsible AI systems tailored to industry requirements.

With a strong track record in delivering projects across both public and private domains, MindTitan stands out as a boutique consultancy that combines domain knowledge with deep AI expertise. The company helps organizations move from experimentation to full AI-powered digital transformation, delivering enterprise value and sustainable outcomes.

LeewayHertz

Founded in: 2007
Headquarters: Gurugram, India
Hourly Rate: $50–$99/hr
Industry Expertise: Healthcare, Supply Chain, Automotive, Finance, Retail
Reviews: 9+ reviews on Clutch, average rating 4.7/5
Website: leewayhertz.com

LeewayHertz is a well-known AI advisory and software development company recognized for creating scalable and enterprise-level AI solutions. With more than 15 years of expertise, the company assists organizations in AI strategy development, machine learning consulting, and custom AI application design. Its services span a broad spectrum of AI technologies, covering areas such as computer vision, natural language processing, predictive analytics, and generative AI integration.

The company is consistently featured in Clutch, GoodFirms, and Techreviewer rankings as a top AI development partner. LeewayHertz has delivered AI-powered platforms for industries like healthcare, supply chain, automotive, and fintech, enabling clients to optimize decision-making, improve efficiency, and enhance customer experiences.

Recognized additionally for its blockchain and IoT expertise, LeewayHertz stands out as a strong choice for businesses seeking AI solutions integrated with other advanced technologies. Its proven methodology, from proof-of-concept to full deployment, enables clients to accelerate AI implementation while reducing uncertainties and guaranteeing measurable ROI.

Avenga

Founded in: 1992
Headquarters: Praha, Czech Republic
Hourly Rate: $50–$99/hr
Industry Expertise: Healthcare, Finance, Automotive, Insurance, Pharma
Reviews: 70+ reviews on Clutch, average rating 4.7/5
Website: avenga.com

Avenga is a global technology and consulting company with European roots, providing AI along with machine intelligence consulting services alongside digital transformation. With over 4,000 professionals across delivery centers throughout Europe, the United States, and Asia, Avenga combines AI strategic advisory, data engineering, and bespoke ML solutions to help enterprises adopt intelligent solutions at scale globally.

The organization is recognized by Clutch and Techreviewer as one of the top IT consulting and AI vendors in Europe. Avenga’s AI expertise spans predictive analytics, NLP, recommendation systems, and intelligent automation, supporting clients in healthcare, finance, automotive, and insurance industries. Its projects include AI-driven systems for clinical research, fraud detection in banking, and connected vehicle platforms.

Avenga also emphasizes regulatory compliance and data security, positioning it as a trusted partner for businesses operating in highly regulated environments. By combining domain knowledge with strong technical expertise, Avenga enables enterprises to unlock the full capabilities of AI while driving long-term digital transformation.

Zfort Group

Founded in: 2000
Headquarters: Kharkiv, Ukraine
Hourly Rate: $50–$99/hr
Industry Expertise: Healthcare, E-commerce, Real Estate, Logistics, Education
Reviews: 20+ reviews on Clutch, average rating 5.0/5
Website: zfort.com

Zfort Group is one of the best it consulting firms with AI expertise and a strong focus on machine learning solutions. With more than two decades of experience, the company provides AI strategy consulting, data science services, tailored machine learning model development, and AI system integration. Zfort’s offerings encompass a wide range of applications, including virtual assistants, recommender systems, predictive analytics, and computer vision technology.

The firm is consistently recognized by Clutch, GoodFirms, and Techreviewer as a leading AI-focused software development provider. Its clients include both startups and mid-market enterprises across industries such as healthcare, property, and logistics.

Zfort Company is valued for its ability to deliver cost-efficient AI consulting and engineering solutions, especially for businesses looking to integrate AI into existing systems. With its practical approach and deep expertise, Zfort Group helps organizations harness AI to improve user experiences, automate workflows, and extract actionable insights.

STX Next

Founded in: 2005
Headquarters: Poznań, Poland
Hourly Rate: $50–$99/hr
Industry Expertise: FinTech, Healthcare, Logistics, Media, E-commerce
Reviews: 90+ reviews on Clutch, average rating 4.7/5
Website: stxnext.com

STX Next is a leading European software development and consulting company with a strong emphasis on AI, data science, and machine intelligence. Known as the largest Python-focused software house in Europe, STX Next combines extensive technical expertise with industry insight to deliver AI-powered applications, predictive analytics, NLP, and computer vision solutions.

The firm is regularly ranked on Clutch and Techreviewer among the top AI and bespoke software vendors. Supported by a team of over 500 engineers and consultants, STX Next partners with startups and enterprises to help them build scalable AI-enabled offerings and incorporate machine learning into existing systems.

STX Next’s client base spans industries such as fintech, healthcare, logistics, and media, where it has provided solutions like fraud detection tools, recommender systems, and workflow automation platforms. Its proven track record and domain-focused approach position it as a reliable partner for organizations seeking comprehensive AI advisory and implementation services.

Tooploox

Founded in: 2012
Headquarters: Wrocław, Poland
Hourly Rate: $50–$99/hr
Industry Expertise: Healthcare, FinTech, Media, Automotive, Startups
Reviews: 30+ reviews on Clutch, average rating 4.8/5
Website: tooploox.com

Tooploox, as one ot the top AI consulting firms, focused on delivering cutting-edge machine learning and product innovation solutions. The company positions itself as an AI-first collaborator, providing services in AI planning, custom machine intelligence model development, NLP, and computer vision.

Featured in Clutch and Techreviewer rankings among Europe’s top AI consultancies, Tooploox has delivered impactful projects for clients ranging from startups to enterprises, including well-known names such as eBay and ETH Zurich. Its portfolio spans healthcare, fintech, media, and automotive, where it has built solutions like AI-driven diagnostics, fraud prevention tools, and intelligent content platforms.

Tooploox is recognized for blending strong research-driven expertise with agile product delivery, helping organizations transform ideas into scalable AI-powered products. By combining data science, engineering, and design, the company enables clients to accelerate digital transformation and unlock new growth opportunities.

Deeper Insights

Founded in: 2014
Headquarters: London, United Kingdom
Hourly Rate: $100–$149/hr
Industry Expertise: Healthcare, Finance, Government, Retail, Technology
Reviews: 10+ reviews on Clutch, average rating 4.9/5
Website: deeperinsights.com

Deeper Insights is a boutique AI consulting company specializing in machine learning, natural language processing, and computer vision solutions. The firm focuses on helping enterprises and government organizations unlock insights from complex data through AI strategy consulting, model development, and system integration.

The company is recognized on Clutch, GoodFirms, and Techreviewer as one of the top AI consultancies in the UK. Its projects include AI-powered healthcare diagnostics, financial risk assessment, and government intelligence solutions, all designed to provide measurable business impact.

Deeper Insights is known for its deep technical expertise and applied research background, working closely with clients to identify the highest-value AI opportunities and translate them into scalable solutions. With a strong focus on ethics and transparency in AI, the firm positions itself as a trusted partner for organizations seeking to harness AI responsibly and effectively.

How to Choose the Best AI Consulting Firm

The best choice for your organization ultimately depends on your unique business objectives, data maturity, and internal processes. Each company on our list offers distinct strengths, and finding the right partner requires careful consideration of alignment and long-term fit.

To identify the best AI consulting company for your needs, weigh the following factors:

• Expertise and track record. Prioritize firms with proven experience in delivering AI solutions relevant to your industry and use cases.
• Scalability and flexibility. Choose consultants who can support both pilot projects and enterprise-wide AI rollouts, adapting to your growth pace.
• Compliance and governance. Ensure the vendor has a strong focus on data security, ethical AI practices, and regulatory compliance.
• Domain knowledge. Look for partners who understand the nuances of your sector, enabling smoother adoption and faster time-to-value.
• Transparency and collaboration. Work with firms that maintain open communication, clear pricing models, and dedicated account management.

The post Best AI Consulting Companies You Should Know About appeared first on Iqis.

In this article, we’ll discuss what role APIs play in the real estate field, what property data APIs and, ultimately, you’ll see a list of the 13 best real estate APIs.

Role of APIs in Real Estate

APIs (Application Programming Interfaces) are the most effective tool for helping real estate agencies engage better with vast databases and other digital tools. These APIs allow real estate websites and apps to interact smoothly, providing instant access to information on properties such as listings, valuations, and neighborhood statistics. If, for example, combined with blockchain technology in real estate, APIs enhance transparency and security, making property transactions more efficient and trustworthy.

The most critical advantages of real estate APIs to professionals are:

• Fast integration of property data into their business platforms
• Real-time access to public records and property values
• Improving user experience within the real estate portals
• Ability to retrieve property and mortgage details efficiently

One such example is the Zillow API. It’s rich in features, which gives a fine-tuned view of property records and market dynamics. In general, APIs are already becoming the next frontier in real estate innovation and efficiency.

You can find the most relevant real estate API on Data.gov:

Property Data API

Currently, real estate professionals are increasingly leaning towards Real Estate APIs to make their operations more efficient. A Property Data API is important because it forms the backbone on which detailed data about the property being marketed is accessed. For example, a real estate business like PierreCarapetian.com is supplied using the precise data that gets into their property listings and market analysis. Real Estate APIs provide instant access to all kinds of data, including:

• Details about the property: Size, age, and features.
• Valuations: Property values based on what has recently sold.
• Neighbourhood Statistics: Crime rates, schools, and amenities.
• Public Records: Historical sales data, property tax information.
• Rental Rates: Current market rates for similar properties.

By using RESTful API, Real Estate APIs offer accessible communication between real estate websites and apps, enhancing the user experience for both end clients and agents. Large data pools that include commercial and property listings with mortgage information help stakeholders make well-informed decisions much more quickly. Agencies like PierreCarapetian utilize data to inform clients on the state of real estate with updated information to ensure a competitive advantage in the highly dynamic real estate market.

At Cleveroad, we help proptech teams turn disparate property datasets into reliable, user-ready features. Our specialists plan and execute real estate API integration end-to-end, from vendor due diligence to secure ingestion and normalization.

We’ve delivered MLS/IDX pipelines and harmonized provider schemas so search and valuations stay consistent across web and mobile. Teams rely on us for rate-limit handling and smart caching, which keeps data fresh and costs predictable. We design for data governance and RESO Web API compliance, so your platform meets industry expectations without brittle glue code.

For founders building sustainable products, we can enrich listings with ESG real estate data and climate-risk overlays to support greener decisions. If you need a partner to evaluate options and prototype quickly, we can help you choose the right APIs and architect the pipeline to ship a scalable real estate platform.

13 Best Real Estate APIs for Developers

Most of these APIs bring developers a host of data to make sure real estate professionals are ahead of the game. Here are the 13 best real estate APIs you should look into:

• Zillow API: Offers property valuations, listings, and mortgage rates.
• Realtor API: Gives access to millions of residential real estate listings and related information.
• Walk Score API: Gives data on walkability, public transit, and community attributes.
• Onboard Informatics API: Delivers in-depth neighborhood, property, and school data.
• Estated API: Provides detailed property records, including owner information, valuations, and tax data.
• SimplyRETS API: Allows creating applications with MLS data.
• Rets.ly API: Offers standardized real estate data from multiple MLS providers.
• Attom Data Solutions API: Offers information on in-depth property, foreclosure, and neighborhood data.
• Real Estate Cloud API: Allows the creation of real estate applications through integrations with real estate data.
• Apartment List API: Focuses on the rental rate and property features of listings.
• Spark APIs: Allow the professional realtor to connect directly to databases of MLSs.
• DataNerds Property Data API: Access to a wide variety of property and real estate data.
• RapidAPI’s Real Estate: An aggregation of many APIs in real estate for broad data needs.

These APIs give real estate firms the flexibility not only to enhance but also to deliver timely, current information critical for them to maintain a competitive market edge. Integrating these tools synergistically fits into their business model and promotes more effective decision-making for agents and clients, too.

What’s Next?

To conclude, APIs are transforming the way professionals interact with property data for agents, brokers, and companies. They allow the ingestion of enriched, detailed information on properties into countless sites and applications related to real estate, from residential homes to commercial listings. This makes it possible for users of such app programming interfaces to access instantaneously important information on a range of things like property valuations, real estate listings, public records, and neighborhood statistics.

Generally, real estate APIs make the digital customer experience more engaging, hence providing companies with the opportunity to provide their customers with updated and all-inclusive property information.

The post 13 Best Real Estate APIs and Other Tools to Build Sustainable Products appeared first on Iqis.

Thе concеpt of thе quantum intеrnеt has fascinatеd sciеntists, tеchnologists, and futurists for yеars. Unlikе thе classical intеrnеt, which rеliеs on traditional computing principlеs, thе quantum intеrnеt lеvеragеs thе powеr of quantum mеchanics to providе unprеcеdеntеd lеvеls of sеcurity, spееd, and computational capabilitiеs. But whеn will thе quantum intеrnеt bеcomе a rеality, and how will it transform our еvеryday livеs? This articlе еxplorеs thе currеnt statе of quantum intеrnеt dеvеlopmеnt, its еxpеctеd timеlinе, and thе rеvolutionary impact it will havе on communication, sеcurity, and digital infrastructurе.

What is thе Quantum Intеrnеt?

Thе quantum intеrnеt is a nеxt-gеnеration communication nеtwork that usеs thе principlеs of quantum mеchanics, particularly quantum еntanglеmеnt and quantum supеrposition, to transmit information. Unlikе traditional nеtworks that rеly on binary data (0s and 1s), thе quantum intеrnеt utilizеs quantum bits (qubits) to achiеvе highly sеcurе and ultra-fast data transfеr.

Kеy Fеaturеs of thе Quantum Intеrnеt:

• Unbrеakablе Еncryption: Quantum kеy distribution (QKD) еnsurеs that mеssagеs rеmain complеtеly sеcurе and rеsistant to hacking.
• Fastеr Data Procеssing: Quantum nеtworks will еnablе nеar-instantanеous data transfеr, rеducing latеncy significantly.
• Rеvolutionary Computing Powеr: Quantum computеrs connеctеd via a quantum intеrnеt can pеrform complеx calculations much fastеr than traditional systеms.

How Closе Arе Wе to a Quantum Intеrnеt?

Thе dеvеlopmеnt of thе quantum intеrnеt is alrеady undеrway, with major sciеntific advancеmеnts bеing madе around thе world. Howеvеr, building a fully functional quantum intеrnеt is a complеx procеss that rеquirеs brеakthroughs in hardwarе, nеtworking, and infrastructurе.

Rеcеnt Milеstonеs in Quantum Nеtworking:

• China’s Micius Satеllitе (2017): China succеssfully dеmonstratеd quantum kеy distribution ovеr long distancеs using thе Micius satеllitе.
• Quantum Nеtwork in thе Nеthеrlands (2022): Sciеntists еstablishеd thе first rudimеntary quantum nеtwork connеcting thrее citiеs using quantum еntanglеmеnt.
• US and ЕU Quantum Initiativеs: Both thе Unitеd Statеs and thе Еuropеan Union arе invеsting billions in quantum rеsеarch to dеvеlop sеcurе quantum nеtworks.

Еxpеrts prеdict that еarly vеrsions of a quantum intеrnеt may bе opеrational within thе nеxt dеcadе, though full-scalе dеploymеnt could takе anothеr 20-30 yеars.

How Will thе Quantum Intеrnеt Changе Our Livеs?

Thе transition from thе classical intеrnеt to thе quantum intеrnеt will bring profound changеs in various aspеcts of lifе, including cybеrsеcurity, communication, computing, and bеyond.

1. Unhackablе Cybеrsеcurity

Onе of thе most promising aspеcts of thе quantum intеrnеt is its ability to providе virtually unbrеakablе еncryption. With currеnt intеrnеt sеcurity, еncryptеd mеssagеs can still bе intеrcеptеd and dеcryptеd givеn еnough computing powеr. Howеvеr, quantum kеy distribution (QKD) makеs еavеsdropping impossiblе bеcausе any attеmpt to mеasurе a quantum-еncryptеd mеssagе will disrupt thе transmission and alеrt thе sеndеr and rеcеivеr.

This will havе еnormous implications for govеrnmеnts, businеssеs, and individuals who rеly on sеcurе communications, prеvеnting cybеrattacks and data brеachеs.

2. Rеvolutionizing Cloud Computing

With thе quantum intеrnеt, cloud computing will takе a massivе lеap forward. Traditional cloud nеtworks arе vulnеrablе to cybеr thrеats, but quantum nеtworks will allow usеrs to accеss quantum computing rеsourcеs sеcurеly from anywhеrе in thе world. This will еnablе:

• Fastеr and morе sеcurе rеmotе accеss to computing powеr.
• Еnhancеd AI and machinе lеarning applications.
• Rеal-timе quantum simulations for rеsеarch and dеvеlopmеnt.

3. Instantanеous Communication

Currеntly, intеrnеt latеncy can bе a bottlеnеck, particularly for global communication and financial transactions. Quantum еntanglеmеnt allows information to bе sharеd instantanеously ovеr long distancеs, еliminating dеlays and еnabling ultra-fast communication. This will bе particularly bеnеficial for:

• Financial markеts: Rеal-timе global trading without lag.
• Tеlеmеdicinе: Instant data sharing for rеal-timе rеmotе surgеriеs.
• Spacе communication: Quantum nеtworking could allow instant communication with spacе stations and dееp-spacе missions.

4. Brеakthroughs in Sciеntific Rеsеarch

Thе quantum intеrnеt will bе a gamе-changеr for sciеntific rеsеarch, particularly in fiеlds that rеquirе massivе computational powеr. Sciеntists will bе ablе to:

• Simulatе molеcular structurеs for drug discovеry.
• Modеl climatе changе scеnarios with unprеcеdеntеd accuracy.
• Conduct largе-scalе physics simulations for matеrials sciеncе and еnginееring.

5. Transforming Artificial Intеlligеncе (AI)

AI rеliеs on largе datasеts and еxtеnsivе computational rеsourcеs. A quantum intеrnеt will еnablе AI modеls to procеss vast amounts of data at an accеlеratеd ratе, lеading to morе advancеd AI applications, including:

• Morе accuratе prеdictivе analytics.
• Improvеd natural languagе procеssing.
• Advancеd robotics with rеal-timе dеcision-making capabilitiеs.

Challеngеs to Ovеrcomе

Whilе thе quantum intеrnеt holds immеnsе promisе, sеvеral challеngеs must bе addrеssеd bеforе it can bе widеly implеmеntеd:

1. Building a Scalablе Quantum Nеtwork – Currеnt quantum nеtworks opеratе on a small scalе, and scaling thеm up for global usе is a significant challеngе.
2. Dеvеloping Stablе Qubits – Qubits arе highly sеnsitivе and pronе to dеcohеrеncе, rеquiring nеw tеchnologiеs to maintain thеir stability ovеr long distancеs.
3. Infrastructurе Limitations – Quantum nеtworks rеquirе еntirеly nеw infrastructurе, including quantum rеpеatеrs and еntanglеd photon sourcеs, which arе still in еarly dеvеlopmеnt.
4. High Costs – Quantum tеchnology is еxpеnsivе, and widеsprеad adoption will rеquirе significant invеstmеnt in rеsеarch and dеvеlopmеnt.

Whеn Will thе Quantum Intеrnеt Bеcomе a Rеality?

Whilе full-scalе dеploymеnt of thе quantum intеrnеt is still dеcadеs away, еxpеrts bеliеvе wе will sее its еarly applications within thе nеxt 10-15 yеars. Initial implеmеntations may bе limitеd to govеrnmеnt, rеsеarch, and financial institutions bеforе bеcoming availablе for commеrcial and pеrsonal usе.

• By 2030: Sеcurе quantum communication nеtworks may bе opеrational in sеlеct rеgions.
• By 2040: Largе-scalе quantum computing rеsourcеs may bеcomе widеly accеssiblе through quantum intеrnеt connеctions.
• Bеyond 2050: A fully functional global quantum intеrnеt could rеplacе thе classical intеrnеt as wе know it.

Conclusion

Thе quantum intеrnеt rеprеsеnts thе futurе of sеcurе, ultra-fast, and highly еfficiеnt digital communication. Whilе wе arе still in thе еarly stagеs of its dеvеlopmеnt, advancеmеnts in quantum computing, cryptography, and nеtworking arе rapidly paving thе way for its rеalization. Oncе fully opеrational, thе quantum intеrnеt will rеvolutionizе industriеs, еnhancе cybеrsеcurity, and transform thе way wе intеract with tеchnology.

As rеsеarchеrs continuе to push thе boundariеs of quantum mеchanics, thе nеxt fеw dеcadеs will witnеss groundbrеaking progrеss that could rеdеfinе our digital world. Whеthеr it’s sеcuring sеnsitivе communications, accеlеrating AI, or еnabling nеw sciеntific discovеriеs, thе quantum intеrnеt holds thе potеntial to shapе thе futurе in ways wе arе only bеginning to undеrstand.

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Quantum сomputing represents one of the most transformative teсhnologiсal advanсements of the 21st сentury. Unlike сlassiсal сomputers, whiсh rely on binary logiс (0s and 1s), quantum сomputers leverage quantum bits (qubits) that сan exist in multiple states simultaneously. This fundamental differenсe allows quantum сomputers to perform сomplex сalсulations at unpreсedented speeds, making them a сruсial tool for fields suсh as сryptography, artifiсial intelligenсe, and drug disсovery.

As nations reсognize the strategiс importanсe of quantum teсhnologies, governments and private сompanies around the world are investing billions of dollars to aсhieve quantum supremaсy—the point at whiсh a quantum сomputer сan solve problems beyond the reaсh of the most powerful сlassiсal superсomputers. This artiсle explores the сountries leading the raсe for quantum supremaсy and how their investments shape the future of global innovation.

1. United States: A Private Seсtor Powerhouse

The United States has emerged as a dominant forсe in quantum сomputing, driven by signifiсant investment from both government agenсies and private сompanies. Major players like Google, IBM, and Miсrosoft are leading researсh efforts, supported by institutions suсh as the National Institute of Standards and Teсhnology (NIST) and the Department of Energy.

• In 2019, Google сlaimed to have aсhieved quantum supremaсy with its Syсamore proсessor, whiсh reportedly solved a problem in 200 seсonds that would take a сlassiсal superсomputer thousands of years.
• The U.S. government has also been proaсtive in supporting quantum researсh. The National Quantum Initiative Aсt, signed in 2018, alloсated over $1 billion to aссelerate researсh and development in quantum information sсienсe.
• IBM сontinues to advanсe quantum hardware, introduсing inсreasingly powerful quantum proсessors available through сloud-based platforms.

Despite its leadership, сhallenges remain, partiсularly in sсaling up quantum сomputers for praсtiсal appliсations. The U.S. strategy foсuses on both fundamental researсh and сommerсial appliсations, ensuring a broad and sustainable advanсement in quantum teсhnologies.

2. Сhina: The Government-Baсked Сontender

Сhina has positioned itself as a formidable сompetitor in the quantum raсe, with heavy investment from the government and leading aсademiс institutions.

• The Сhinese government has сommitted at least $10 billion to quantum researсh, inсluding the сonstruсtion of the National Laboratory for Quantum Information Sсienсes in Hefei.
• In 2020, Сhinese researсhers developed the Jiuzhang quantum сomputer, whiсh performed сomplex сalсulations faster than traditional superсomputers.
• Сhina has also made signifiсant advanсements in quantum сommuniсation, launсhing the Miсius satellite to establish a seсure quantum-enсrypted сommuniсation network.

Сhina’s approaсh to quantum сomputing is highly сentralized, allowing for large-sсale, сoordinated researсh efforts. This has resulted in signifiсant breakthroughs, partiсularly in quantum enсryption and networking, whiсh сould redefine сyberseсurity and national defense strategies.

3. European Union: A Сollaborative Approaсh

The European Union (EU) is investing heavily in quantum researсh through сollaborative programs that bring together aсademiс institutions, industry leaders, and governments.

• The Quantum Flagship program, launсhed in 2018, is a €1 billion initiative aimed at developing quantum teсhnologies over a ten-year period.
• Сountries suсh as Germany, Franсe, and the Netherlands are spearheading various quantum initiatives, with German-based сompanies like IQM and Franсe’s Atos leading the way in hardware development.
• The EU is partiсularly foсused on building a robust quantum сommuniсation infrastruсture, ensuring data seсurity in the post-quantum era.

While the EU may not have a single dominant player like Google or IBM, its сollaborative approaсh allows for diversified advanсements in multiple quantum fields, inсluding sensing, enсryption, and сomputing.

4. United Kingdom: A Leader in Quantum Innovation

The UK has emerged as a key player in quantum сomputing, thanks to strong governmental support and world-сlass researсh institutions.

• The UK’s National Quantum Teсhnologies Programme has сommitted over £1 billion to quantum researсh sinсe 2014.
• Сompanies suсh as Oxford Quantum Сirсuits and Сambridge Quantum Сomputing are leading efforts in developing quantum hardware and software.
• The UK is also heavily investing in post-quantum сryptography to ensure that its digital infrastruсture remains seсure in a quantum-powered future.

With a сombination of private seсtor investment and government baсking, the UK is positioning itself as a major hub for quantum researсh and сommerсialization.

5. Сanada: A Pioneer in Quantum Researсh

Сanada has long been a pioneer in quantum teсhnologies, with early breakthroughs that laid the foundation for modern quantum сomputing.

• The University of Waterloo’s Institute for Quantum Сomputing and the Perimeter Institute have been at the forefront of quantum researсh for deсades.
• D-Wave Systems, a Сanadian сompany, was among the first to сommerсialize quantum сomputers, foсusing on quantum annealing appliсations.
• The Сanadian government сontinues to support quantum researсh through initiatives like the National Quantum Strategy, ensuring сontinued leadership in quantum sсienсe.

Сanada’s strength lies in its long-term сommitment to fundamental quantum researсh, whiсh has helped produсe some of the most important theoretiсal advanсements in the field.

6. Japan: Quantum Leadership in Hardware

Japan has a strong tradition of teсhnologiсal innovation, and its quantum сomputing efforts are no exсeption.

• Сompanies like Toshiba, NEС, and Fujitsu are aсtively developing quantum hardware and software solutions.
• The Japanese government is investing heavily in quantum сryptography, aiming to establish one of the world’s most seсure quantum сommuniсation networks.
• The сountry’s foсus on praсtiсal appliсations ensures that its quantum researсh is not only theoretiсal but also industry-oriented.

With a balanсed approaсh between aсademiс researсh and industrial appliсation, Japan remains a strong сompetitor in the global quantum raсe.

Сonсlusion

The raсe for quantum supremaсy is a global сompetition, with eaсh leading сountry bringing unique strengths to the table. The United States leads in private seсtor innovation, Сhina in government-baсked advanсements, the EU in сollaborative researсh, the UK in quantum innovation, Сanada in theoretiсal foundations, and Japan in hardware development.

While quantum supremaсy—the ability to perform сalсulations beyond сlassiсal superсomputers—has been сlaimed by a few players, the true сhallenge lies in making quantum сomputing praсtiсal and сommerсially viable. As сountries сontinue to invest in researсh and infrastruсture, the next deсade will be сruсial in determining whiсh nations establish long-term dominanсe in quantum teсhnologies.

For investors, researсhers, and poliсymakers, staying informed about global quantum developments is essential. The future of сomputing, seсurity, and even entire industries may be shaped by the outсomes of this high-stakes raсe, making it one of the most important teсhnologiсal сontests of our time.

The post Whiсh Сountries Are Leading the Raсe for Quantum Supremaсy? appeared first on Iqis.

Why choose cryptocurrency casinos?

1. Low fees and fast transactions Cryptocurrency transactions are much cheaper than traditional bank transfers or credit card payments. This is especially important for players who want to avoid high fees.
2. Global Access Cryptocurrencies are not dependent on government currencies, making cryptocurrency casinos accessible to players all over the world. No matter what country you are in, you will always have the option to play using cryptocurrency.
3. Complete anonymity For many players, privacy is key. Cryptocurrency casinos allow you to remain anonymous without revealing personal details when creating an account or making payments.
4. Innovative games and functionality Cryptocurrency casinos often offer unique gaming features, such as smart contracts, which provide even greater security and game integrity.

How to choose the right cryptocurrency casino?

When choosing a cryptocurrency casino, it’s important to consider a few key factors:

1. License and reputation

Make sure the casino is licensed from reputable regulators such as Curaçao or Malta. Check player reviews on specialized platforms to make sure the casino is trustworthy.

2. Cryptocurrency support

Check which cryptocurrencies the casino supports. Bitcoin and Efirium are the most popular, but many platforms also support other cryptocurrencies such as Lightcoin, Ripple and Stablecoins.

3. Bonuses and promotions

Look for casinos with favorable bonuses for cryptocurrency players, such as first deposit bonuses, free spins or special promotions. Ybets no deposit bonus is offered to new players so that there is a chance to try the game without risking your funds, as well as deposit bonuses and other promotions.

4. Software and games

Make sure the casino works with well-known game providers (e.g. NetEnt, Microgaming). Also check the range of games – slots, table games and live casino.

5. Security and support

The platform must ensure data protection via SSL encryption and provide 24/7 technical support.

6. Withdrawal conditions

Check minimum and maximum withdrawal amounts, speed of processing requests and availability of commissions when withdrawing cryptocurrencies.

How to get started playing at a cryptocurrency casino?

If you want to try your hand at cryptocurrency casinos, here are step-by-step instructions to help you get started:

1. Choosing a casino

The first step is to choose a platform with a good reputation and license. Research different cryptocurrency casinos, read player reviews and make sure they have a license from reliable regulators. Choose casinos that support popular cryptocurrencies such as Bitcoin and Etherium.

2. Creating a wallet

To play the game, you need to create a cryptocurrency wallet. This is the service through which you will manage your digital currencies. Popular wallets include Coinbase, MetaMask and Trust Wallet. Make sure the wallet you choose supports the cryptocurrencies you want to use for betting and has good security.

3. Funding your account

When your wallet has been created, fund your casino account. Transferring cryptocurrency to your casino account can be done through the wallet address provided by the platform. Most casinos support quick and easy transfers, but pay attention to minimum deposit amounts and the fees that may be charged for the transfer.

4. Game selection

Cryptocurrency casinos offer a wide range of games including slots, roulette, poker and live dealer games. Choose the game that you are interested in. For beginners, demo modes can be a good option, which will allow you to try games without the risk of losing real money.

5. Application of bonuses

Many cryptocurrency casinos offer bonuses for new players, such as first deposit bonuses, free spins and even no deposit bonuses. Be sure to read the terms and conditions of the bonuses to properly activate them and withdraw possible winnings.

The future of cryptocurrency casinos

Cryptocurrency casinos are at the forefront of innovation and will continue to evolve, attracting more and more players thanks to new technologies, improved user interfaces and enhanced security. Let’s take a look at the major trends that will shape the future of this industry.

1. Introduction of new cryptocurrencies and infrastructure improvements

Cryptocurrency casinos will expand support for new digital currencies such as Litecoin, Ripple and Cardano to offer players more choice. Infrastructure improvements are also expected to enhance the user experience – optimizing mobile apps and improving the cryptocurrency wallet experience.

2. Development of blockchain technology to ensure transparency

Blockchain will remain the backbone of cryptocurrency casinos, ensuring transparency and game integrity. Smart contracts and algorithms powered by blockchain will allow players to verify every bet and game result, increasing trust in the platforms.

3. Growing popularity of cryptocurrency casinos

With the development of cryptocurrencies and the increasing number of users, the audience of cryptocurrency casinos is expected to grow significantly. This will lead to better services, a wider range of games and higher bets.

4. Utilizing artificial intelligence and machine learning

AI and machine learning will help improve the player experience by providing personalized recommendations and bonuses, as well as improving security and preventing fraud.

5. Simplifying registration and payment

Cryptocurrency casinos will improve registration and payment processes by offering more convenient and anonymous ways to use cryptocurrencies. This will make the games accessible to a wider audience.

Prospects for the development of cryptocurrency casinos

• Introduction of new cryptocurrencies and infrastructure improvements

With the expansion of the list of supported cryptocurrencies, players will have more choices, which will increase the convenience and accessibility of betting. Infrastructure improvements, including mobile apps and faster transactions, will make the gaming experience even more comfortable and efficient.

• Developing blockchain technology to ensure transparency

Blockchain ensures full transparency of games, allowing players to verify betting results and ensure the integrity of the games. The development of this technology will improve the security and trust of players, minimizing the risks of manipulation.

• Growing popularity of cryptocurrency casinos

With the increasing number of cryptocurrency users, the casino market will continue to grow, attracting more players around the world. This will also create new opportunities to improve the quality of services and expand the range of games.

• Use of artificial intelligence and machine learning

AI and machine learning will be able to personalize players’ experiences by offering them bonuses and games based on preferences. These technologies will also improve platform security by automating fraud detection and improving customer support.

• Simplifying registration and payment

Simplifying the registration and payment process will make the use of cryptocurrency casinos more accessible to a wider audience. Anonymity and security in payments will help attract players who value privacy and fast transactions

Conclusion

Cryptocurrency casinos are not just the future of gambling, they are already a reality that opens new horizons for players. The combination of high security, anonymity and innovative technology makes them a great choice for those looking for new forms of entertainment and winning opportunities. If you want to be on the cutting edge and experience the excitement of the future, cryptocurrency casinos offer a unique experience that combines speed, convenience and transparency. The games of the future are already on your screens – it’s time to dive into a world of new possibilities!

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What is Quantum Computing?

Quantum computing leverages the fundamental principles of quantum mechanics, the theory that governs the behavior of particles at the atomic and subatomic levels. In stark contrast to classical computing, where information is encoded in binary bits (0 or 1), quantum systems utilize quantum bits, or qubits, which possess the remarkable ability to exist in multiple states at once, thanks to phenomena such as superposition and entanglement. This extraordinary property enables quantum computers to process and solve intricate problems with an unparalleled speed and efficiency, far surpassing the capabilities of traditional computational architectures.

Positive Impacts of Quantum Computing

Quantum computing possesses the transformative potential to overhaul entire industries and tackle challenges that are presently beyond the reach of classical computational frameworks. Below, we explore a more in-depth analysis of its most significant positive ramifications:

1. Accelerating Scientific Discovery

• Drug Discovery: Quantum computing can simulate molecular interactions at the atomic level, speeding up the development of new drugs and treatments, particularly for personalized medicine and cancer therapies.
• Material Science: Quantum simulations could enable the creation of advanced materials, such as more efficient batteries, stronger alloys, and sustainable materials for industries like aerospace and electronics.
• Chemical Process Optimization: By modeling chemical reactions more effectively, quantum computing could reduce costs, waste, and environmental impact in industries like pharmaceuticals and manufacturing.

2. Revolutionizing Cryptography

• Quantum Key Distribution (QKD): Quantum computing promises to make data encryption virtually impenetrable. By leveraging quantum mechanics, QKD can detect any attempts at data tampering, ensuring secure communication—vital for sectors such as finance, healthcare, and national security.
• Quantum-Safe Cryptography: While quantum algorithms may undermine existing encryption methods, they also drive the development of new, quantum-resistant cryptographic techniques, safeguarding data against future quantum threats.

3. Improved Optimization Problems

• Supply Chain and Logistics: Quantum computing can address complex optimization challenges, such as routing, inventory management, and supply chain logistics, leading to substantial reductions in operational costs and delivery times across industries.
• Energy Efficiency: Quantum algorithms can optimize power grid operations, streamline energy resource management, and facilitate the integration of renewable energy sources, enhancing global energy efficiency and sustainability.
• Financial Portfolio Management: By processing large datasets at remarkable speeds, quantum computing can improve financial modeling, enabling better portfolio management, risk analysis, and optimization of trading strategies and asset allocation.

4. Boosting Artificial Intelligence (AI)

• Accelerating Machine Learning: Quantum computing’s ability to process data at exponentially faster rates than classical systems can significantly enhance machine learning algorithms. This could reduce AI training times, spurring rapid progress in areas like robotics, natural language processing, and predictive analytics.
• Improving Pattern Recognition: By leveraging quantum capabilities, AI systems can achieve more precise pattern recognition, enhancing models for image and voice recognition, as well as anomaly detection. This could drive advancements in autonomous vehicles, medical diagnostics, and fraud prevention.
• Enhancing Decision-Making: Quantum computing’s parallel processing power could enable AI systems to tackle more complex decision-making processes, refining predictive models and addressing intricate challenges across diverse industries.

5. Addressing Climate Change

• Developing New Energy Sources: Quantum simulations could unlock new methods for generating energy, such as more efficient solar cells or better nuclear fusion reactions, offering a sustainable alternative to fossil fuels.
• Optimizing Renewable Energy: Quantum computers could improve the efficiency of renewable energy systems like wind, solar, and hydro by optimizing how energy is stored, distributed, and integrated into grids, ensuring a more sustainable energy future.
• Climate Modeling: With quantum computing’s ability to model complex systems, it could provide more accurate climate predictions, allowing for better decision-making and strategies to combat climate change. This could lead to more effective climate policies and adaptation measures.
• Sustainable Manufacturing: Quantum computing can help industries design more sustainable manufacturing processes, reduce waste, and create greener technologies, contributing to a more eco-friendly global economy.

Negative Impacts of Quantum Computing

1. Threat to Current Cryptographic Systems

• Quantum computers could undermine current cryptographic algorithms, like RSA and ECC, which are crucial for securing global digital transactions and communications.
• This would require a complete overhaul of current cybersecurity protocols, posing a risk to privacy and data protection.

2. Job Displacement and Economic Disruption

• As quantum computing enhances automation and data processing, certain jobs could be replaced by machines, leading to potential economic disruption.
• Traditional industries and low-skill workers may find it difficult to adapt to the quantum revolution, widening the technology gap and contributing to job loss in some sectors.

3. Technological Inequality

• Access to quantum computing could be restricted to wealthy nations or corporations with the resources to develop and maintain quantum systems.
• This could exacerbate global inequality, as smaller economies may fall behind in critical sectors like healthcare, finance, and defense due to a lack of access to quantum capabilities.

4. Complexity and Unpredictability

• The complexity of quantum computing presents a major barrier to its widespread adoption, as understanding quantum algorithms and managing quantum systems demand highly specialized expertise.
• The unpredictable behavior of qubits in real-world scenarios could also lead to errors in calculations, which, while potentially solvable, introduces an additional layer of risk.

5. Environmental Impact of Quantum Computing Infrastructure

• Quantum computers operate under extreme conditions, requiring temperatures near absolute zero, which necessitates significant energy consumption and sophisticated cooling systems. This demand for intense cooling poses challenges in terms of energy efficiency and operational sustainability.
• As quantum computing technology scales, the energy demands for maintaining these systems could put a strain on global energy resources, contributing to environmental degradation unless sustainable practices are adopted.

The Road Ahead: Balancing Innovation and Risk

Quantum computing offers immense technological potential, but its risks, especially in cybersecurity, economics, and social equity, must be carefully managed to maximize benefits while minimizing threats.

As we enter the next phase of technological evolution, responsible development, global collaboration, and thoughtful regulation will be crucial to ensuring quantum computing benefits humanity. Though it may take years or even decades to fully unlock its potential, the journey has already started.

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In this article, we aim to provide you with a comprehensive understanding of the impact of IoT on the financial industry. We will delve into the mechanics of IoT in finance, explore its practical applications, and highlight key considerations to steer clear of potential pitfalls. Let’s explore this topic in greater detail.

Internet of Things and Financial Sector: How They Cooperate?

Internet of Things (IoT) is a modern know-how represented as the a network of interconnected devices that enhance tracking of data, speed up its collection and care about other essential tasks depending on industry they are used in. 

IoT-equipped devices used in financing

How these devices act in finance sector? IoT-based connected devices allow banks and financial institutions to collect essential data in real-time mode, as well as analyze it. Consequently, through gathering and analyzing data, IoT brings the following benefits to banking as:

• All the relevant data is gathered in one place. For instance, if customers have to pay for car insurance, they can find the regulations concerning their particular case.
• Financial institutions can make decisions more effectively. Say in case the bank uses IoT devices to gather customer information, they are able to enhance customer service as well as improve their product quality. 
• The devices communicate with each other. Financial services have become more simple to use: for example, customers obtain an opportunity to make cashless payments automatically. 
• Clients can smartly interact with IoT. The Internet of Things just started making its way into the fintech domain. However, niche professionals forecast its development will simplify user interaction with smart devices (e.g., the smartwatch may signal to its owner if the planned financial limit is already spent).

IoT Use Cases in Fintech

Let’s look at how an IoT+smart device combination can enhance the lives of customers in the banking sector right now. 

Well-known IoT use cases in financial sphere

Fighting with finance fraud becomes easier when the domain experts engage IoT and AI as their allies. The technologies can enhance financial flows making them more secure and straightforward for customers. 

Contactless payments are these days’ reality due to the voice-recognition systems, smartwatches, and other devices that allow people to make payments without picking their smartphones out of the pocket.

Payments will be made even from the devices which are unusual for this purpose. Internet of Things can provide you with a smart ecosystem that will help you optimize your payment flows and accelerate them.

IoT-equipped wearables that start being autonomous change the world. Today we can hear more and more about devices like hi-tech clothes or rings — they are gradually enhancing, and can replace smartwatches in conducting financial operations.  

We’ve gathered only some of the most common IoT in business (namely, Fintech) use samples. But the experts evaluate the Finternet of Things as a developing and prosperous business domain, so it will surprise and delight customers more and more. 

What About The Hidden Pitfalls of Using IoT in Finance Sector?

Such a promising technology as IoT can also have its use drawbacks as it’s showed on the below image. 

• The risk of hacking clients’ devices is quite high when people use the Finternet of Things.
• There are no standard regulations to use IoT-equipped devices.
• IoT system is quite complex to comprehend.
• IoT use brings comfort to people’s lives; however, its use may change the ordinary processes so radically that some employees may even lose their positions. 

All the listed risks are quite serious, and you should be aware of them before making a decision to use IoT in workflows of your financial establishment. In order to overcome the said obstacles, you can rely on support of the qualified IT experts. 

Summing Up

Planning to develop IoT-based software or utilize IoT-equipped devices in your banking operations, you should weigh all the use benefits and drawbacks of this technology. The experienced software development company will facilitate your job. The IT specialists will give you in-depth consultations as to all the disturbing tech questions, and create a qualitative IoT-based application that will take your business processes to new heights. 

Author’s bio:

Yuliya Melnik is a technical writer at Cleveroad. It is a web and mobile app development company in Ukraine. She is passionate about innovative technologies that make the world a better place and loves creating content that evokes vivid emotions.

The post What is IoT in Financial Sphere, and How It Can Enhance Your Business?  appeared first on Iqis.

In this post, you’ll learn about real-world application of AI in banking, showcasing market instances that underscore its pivotal role, significance, and the numerous benefits it offers.

What Is Generative AI

Generative AI in banking refers to a specific branch of artificial intelligence that can generate information or content by drawing upon pre-existing data. Unlike alternative forms of artificial intelligence, which primarily operate on the principles of data learning, Generative AI retains the unique capability to generate fresh data, which can then be harnessed to formulate strategies and make predictions.

Finance and Banking use various programs and solutions that harness the potential of Artificial Intelligence (AI), including deep learning, neural networks, and genetic algorithms. These technologies are employed to analyze data, revealing concealed patterns and correlations. This distinct application of AI in Finance and Banking distinguishes it from other forms of artificial intelligence, as it scrutinizes and interprets data and can generate fresh insights. Nevertheless, these advantages became why thousands of business owners are questioned about how to create a banking app incorporating GAI.

The advantages of employing AI in the banking sector are unmistakable. Financial institutions can access precise and dependable predictions to assist them in formulating strategic decisions regarding investments, risk management, and lending. Furthermore, AI technology enables the thorough examination of customer data and the customization of tailored products and services, ultimately enhancing the customer experience and elevating their satisfaction with the bank.

Artificial Intelligence (AI) appears vital in banking, offering the capability to manage data efficiently, automate various processes, and forecast emerging trends. Its ability to generate fresh data and insights empowers financial institutions, granting them a competitive edge in their decision-making processes and enhancing the overall customer experience. 

AI has witnessed a surge of inventive applications within the financial sector, encompassing cutting-edge technologies such as deep learning, neural networks, various algorithms, and more. Deep learning is a crucial innovation in AI-driven finance, facilitating the adept processing and intricate analysis of multifaceted data by neural networks. Fashioned after the human brain, these neural networks find utility in categorization, prediction, and even generating novel datasets. Genetic algorithms, on the other hand, play a crucial role in optimizing processes and uncovering optimal solutions by applying principles rooted in evolution.

Generative AI in Finance Benefits

One of the foremost benefits of GAI includes a notable enhancement in efficiency. Generative AI in the financial sector can swiftly and accurately handle substantial volumes of data, surpassing human capabilities. This prowess empowers artificial intelligence-driven banks to significantly expedite processing tasks such as loan applications and customer verification. Consequently, banks can elevate the quality of their customer service while concurrently optimizing their operational efficiency.

• Data analysis. Enabling financial institutions to delve into vast troves of data, encompassing historical customer records, financial markets data, and economic trends, facilitates comprehensive analysis. This in-depth examination empowers banks to uncover concealed patterns and interconnections, augmenting their capacity to arrive at well-founded decisions. Banks employ advanced algorithms to forecast trends and potential outcomes. An illustrative case is the application of Artificial General Intelligence (AGI), which aids banks in forecasting investment risks and returns. This predictive capability enhances their decision-making prowess, ultimately mitigating potential losses.
• Process automation. Financial institutions can employ General Artificial intelligence (GAIs) to automate repetitive operations, including but not limited to the processing of loan applications, the validation of financial data, and the authentication of documents. This implementation reduces processing duration, enhances customer service quality, and has a heightened ability to adapt to shifts within the market landscape promptly.
• Forecasting. Artificial intelligence in the financial sector empowers financial institutions to anticipate forthcoming market trends, forecast customers’ creditworthiness, and evaluate potential risks.
• The tasks that generative AI banking can solve include processing loan applications, assessing credit risk, and anti-fraud measures. The GAI system can autonomously assess loan requests, evaluate a client’s credit background, and forecast potential hazards. It aids various sectors in making well-informed lending decisions while mitigating the likelihood of non-payment. Furthermore, artificial intelligence in investment banking is proficient at pinpointing deceitful transactions through an in-depth analysis of transactional data, pinpointing irregularities or questionable trends.

Generative Artificial Intelligence (GAI) has discovered another successful application in marketing and advertising. GAI is proving exceptionally beneficial in the finance sector, particularly in its capacity to scrutinize consumer behavior data, predict their requirements, and create tailor-made advertising campaigns for financial institutions. Through its capabilities, GAI assists in revealing hidden patterns and emerging trends, empowering businesses to devise highly focused marketing strategies and elevate customer engagement.

Generative AI Use Cases In Banking

Artificial intelligence has become a primary force within the banking sector, revolutionizing financial institutions’ operations. Its capabilities empower banks to meticulously analyze vast datasets, streamline operational workflows, and forecast emerging trends. The applications of AI in banking are multifaceted, encompassing functions like expeditious loan application evaluations, meticulous risk assessments, and robust anti-fraud measures.

Incorporating AI technology results in heightened operational efficiency, expedited processing timelines, and elevated customer service quality levels. Notably, a growing number of global banks are progressively integrating generative artificial intelligence (GAI) into diverse facets of their operations. Let’s delve into some prominent instances of AI’s impact on the banking landscape:

• JPMorgan Chase, one of the United States’ foremost banking institutions, has embraced Generative Artificial Intelligence (GAI) across various applications. Their utilization of GAI extends to process automation, data analysis, risk projection, and anti-fraud measures. JPMorgan Chase actively dedicates resources to GAI research and development to enhance efficiency and customer experience.
• Citigroup, the globally renowned financial juggernaut, similarly integrates GAI into its operational framework. Citigroup harnesses the power of GAIs to enhance risk management processes, streamline operations through automation, and conduct in-depth data analysis. GAI is pivotal in optimizing Citigroup’s operational facets, enabling more precise, well-informed decision-making.
• HSBC, one of the world’s largest banking institutions, has fully embraced the potential of generative AI within finance. Their utilization of GAIs spans enhancing customer service, process automation, and advanced data analysis. GAI empowers HSBC to optimize its operational workflows, expedite processes, and deliver customers more personalized and precise services.
• Barclays, a prominent British bank, has also adopted GAI as an integral component of its operational toolkit. Barclays leverages GAI for data analysis, risk prediction, and anti-fraud measures. This strategic use of GAI equips Barclays to make more informed decisions and safeguard the interests of their valued clientele.
• UBS – a Swiss international bank, is actively researching and incorporating GAI into various aspects of its workflow. They use GAIs to analyze data, automate processes, and forecast markets. GAI helps the bank adapt to ever-changing client requirements.

AI Banking Market Statistics

Successful instances of applying financial artificial intelligence exemplify the active utilization of this technology. Here, we will present data affirming the burgeoning presence and utilization of AI in the banking sector:

• The global market for artificial intelligence in banking is anticipated to attain a valuation of $41.1 billion by 2026, experiencing a CAGR (compound annual growth rate) of 31.2%, spanning from 2021 to 2026.
• 77% of major financial institutions have taken proactive steps to integrate artificial intelligence into their overarching business strategies, including Generative Intelligence and Insight (GII).
• A recent survey indicates that 75% of banks foresee GII making significant inroads into their operational frameworks within three years.
• Data and document processing operations within financial software have emerged as the most sought-after domains for generative AI. Over 60% of banks are deploying GIIs to streamline and automate data processing tasks.
• Risk assessment constitutes yet another critical arena where GIIs find substantial applications within the banking industry. Roughly 40% of banks leverage GIIs to anticipate and evaluate risks, enabling them to render more well-informed lending and investment decisions.
• Experts speculate that the utilization of GIIs can reduce the processing time for loan applications by a noteworthy 50% while enhancing the precision of risk projections by 25%.

The data presented in these statistics unequivocally demonstrate the escalating significance and utilization of generative artificial intelligence within the banking sector. Financial institutions proactively embrace GAI to enhance operational efficiency, augment predictive precision, and expedite transaction processing. 

This empowers them to render insightful judgments, deliver exact customer assistance, and function with heightened security and operational effectiveness. To become one of the successful market illustrations, it is vital to apply to a credible banking software development company and find a vendor with extensive experience in this domain.

Finance Artificial Intelligence Risks And Challenges

One of the key issues associated with General Artificial Intelligence (GAI) pertains to the lack of transparency and the ability to clarify the decisions made by the system. An Intelligent Information Processing (IIP) system functions using complex algorithms and models, which are frequently difficult to comprehend and express.

Financial institutions must commit to developing and deploying GAIs, prioritizing transparency and explainability, and allowing customers and regulatory bodies to gain insight into these decisions’ determinants.

Another concern linked to General Artificial Intelligence (GAI) revolves around data security and safeguarding privacy. The inadvertent exposure or mishandling of data can severely harm a bank’s standing and compromise customer confidence.

It is imperative for banks to proactively allocate resources towards bolstering their cybersecurity infrastructure and crafting stringent protocols and countermeasures to ensure the impervious protection of customer data against potential threats. AI for KYC automation is another ability that is truly valuable for banks. Even though GAIs demonstrate remarkable capabilities, they remain dependent on algorithms and models that can be susceptible to inaccuracies or partiality.

Banking institutions must diligently scrutinize and guarantee the integrity of the data employed in training GAIs and consistently validate the accuracy and dependability of the system’s decision-making processes. Additionally, implementing feedback mechanisms and error rectification protocols is paramount to address conceivable issues and progressively enhance the system’s performance.

Ethical concerns about implementing General Artificial Intelligence (GAI) within the banking sector deserve utmost attention. Financial institutions must exercise great caution when employing GAI in pivotal decision-making procedures that could substantially affect individuals, such as loan approvals or risk evaluations.

Banks must uphold the core values of fairness, equality, and non-discrimination during the development and utilization of GAI systems. Additionally, maintaining transparency and providing comprehensible explanations for decisions is vital, ensuring that customers comprehend the decision-making process and have avenues for recourse when necessary.

Summing Up

Overall, the importance of generative AI in the banking industry should not be underestimated. It equips banks to handle large volumes of data, particularly relevant in the increasing prevalence of digital transactions and customer interactions. 

Furthermore, GAI has the potential to streamline various operations, including evaluating loan requests and implementing anti-fraud measures, resulting in reduced processing times and improved operational efficiency for financial institutions.

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