CoinStats CEO Narek Gevorgyan on Building a Crypto AI Agent for Real-Time Research

Crypto research has become harder as the market has grown across chains, protocols, wallets, exchanges, and social platforms. A single investment decision can require hours of checking token data, on-chain flows, sentiment, news, liquidity, and portfolio exposure.

CoinStats started as a portfolio tracker, giving users one place to monitor assets across wallets and exchanges. The company is now building a more ambitious product around crypto-specific AI, developer APIs, and agent-ready data access.

In an exclusive interview with BeInCrypto, Narek Gevorgyan, Founder and CEO of CoinStats crypto tracker, discussed why crypto needs domain-specific AI, how CoinStats AI approaches research, and why machine-readable crypto data will become essential as AI agents enter the market.

CoinStats began as a portfolio tracker. What led the push toward an AI-driven crypto research product?

Tracking a portfolio is the easy part. The hard part is understanding what to do next.

Our users were spending hours jumping between X, Discord, Etherscan, news sites, analytics dashboards, and exchange pages just to make one decision. CoinStats already had the data layer in place, including coverage across 120+ chains, market data, on-chain flows, and social context.

AI was the natural next step. Instead of giving users more dashboards, we wanted to help them reach better answers faster.

You are making a strong case for domain-specific AI in crypto. Where do general-purpose models still fall short for serious crypto research?

It mostly comes down to architecture and data.

When a user asks CoinStats AI a question, specialized sub-agents work in parallel. One can pull real-time news. Another can scan social sentiment. Another can read on-chain data across 120+ blockchains. Another can check exchange metrics. Another can analyze the user’s actual portfolio.

Those agents report back, and the system synthesizes the information into one answer with interactive tables and charts, instead of a long wall of text. The model is reading from live sources rather than recalling information from training data. That reduces a large part of the hallucination risk.

We also let users choose the depth of the answer. CoinStats AI has three modes. Deep Research is for full multi-source reports. Backtesting helps users test strategies against historical data. Fast Mode is for quick lookups.

A general model usually gives one style of answer. Crypto research has many different question types.

We tune CoinStats AI around the actual work crypto users do, including token research, wallet analysis, risk checks, smart money tracking, whale activity, contract deployments, KOL sentiment, and macro correlations between things like Fed policy and ETF flows. The difference becomes obvious once the questions become specific.

CoinStats has suggested its AI performs strongly against larger general models on crypto research tasks. What exactly is it doing differently under the hood?

It is a combination of live data access, retrieval, task-specific agents, and crypto-native reasoning.

General models usually lack live on-chain data, so they cannot reliably tell you who is accumulating a token or where liquidity is moving. They also lack real-time market and social context, so they can miss narratives as they form. Their training data can become stale very quickly in a market where a token can launch and move aggressively within days.

They also reason like generalists. Crypto research often requires understanding MEV, slippage, bridge risk, liquidity fragmentation across chains, wallet behavior, exchange flows, and protocol-specific risk.

Privacy is another major point. When a user pastes a wallet address into a general AI model, they may be exposing their holdings to a third-party provider. Crypto users care about this.

That is why we built Private Mode in CoinStats AI. When users turn it on, queries are routed through Venice AI’s encrypted, decentralized system. No third-party AI provider sees the user’s data. Whether someone is researching wallets, analyzing token flows, or looking into positions they prefer to keep private, the information stays between the user and the blockchain.

General models are useful for casual questions. Serious crypto research needs live data, privacy, and crypto-specific context.

How are you thinking about accuracy, trust, and hallucination risk when users may act on the output?

Crypto is a market where loose accuracy can become expensive.

Our approach is built around three principles. First, every claim should be grounded in live data with sources, so users can check the work. Second, Backtesting Mode lets users validate a thesis against historical data before risking capital. Third, we are very clear about the product’s role.

CoinStats AI is a research tool. It is built to support the DYOR process, not replace user judgment. DYOR should be part of the product experience itself, not a disclaimer at the bottom of a page.

On the developer side, CoinStats is also pushing its API and MCP support for AI agents and IDEs. Why is a developer-accessible crypto data layer important?

Crypto has a structural data problem. The information needed to understand a portfolio, market, or on-chain event is fragmented across hundreds of chains, thousands of protocols, dozens of centralized exchanges, and a growing DeFi ecosystem.

Any developer or AI agent trying to reason about crypto has two options. They can spend years solving aggregation themselves, or they can plug into a provider that already does it.

That is the role we see CoinStats playing. We have spent years normalizing data across 300+ exchanges and wallets, every major chain, and a long tail of DeFi positions.

By exposing this through the CoinStats Crypto API and MCP server, developers building AI agents, trading tools, research products, or side projects in environments like Cursor or Claude Code can access portfolio state, market data, news, and on-chain context as usable primitives.

They do not need to rebuild the pipeline before building the product.

MCP is becoming a serious conversation in AI tooling. How do you see CoinStats fitting into a future where crypto workflows are increasingly handled by agents?

Crypto data has always been fragmented. Prices live on one platform. Wallet balances live somewhere else. DeFi positions sit across many protocols. NFTs may sit elsewhere again.

For developers, stitching all of this together is often the hardest part. Teams can spend more time on data plumbing than on the user experience.

That is what we set out to solve with CoinStats API. The coverage spans 100,000+ coins and 200+ exchanges. It extends across 120+ blockchains. DeFi positions are resolved across 10,000+ protocols at the wallet level.

Developers get one access point for the full picture. That changes what a small team can build quickly.

Our MCP server takes the same idea further. AI agents and LLMs can query wallet, DeFi, and portfolio data directly. An IDE-integrated agent can pull a user’s positions, analyze them, and support a workflow without custom adapters.

This is important because crypto tools are evolving. Future workflows will involve agents monitoring risk, rebalancing portfolios, surfacing opportunities, and supporting research. For that to work, the data layer has to be machine-readable, reliable, and complete enough to understand what someone owns and how those assets move on-chain.

Pricing data alone is not enough. Agents need wallet data, DeFi position resolution, and long-term historical context. That is the layer CoinStats API is building.

Crypto growth also depends on how easy it is to build useful products. Every hour developers save on data aggregation can be spent improving the user experience.

If we have this conversation again a year from now, what would you want to have built, improved, or proven about CoinStats by then?

There are three things I would want to see.

First, I would want CoinStats to widen its lead in crypto-specific research against general-purpose AI. The benchmark we released this year is the beginning. We want to expand it, run it more frequently, and keep the methodology open source so anyone in the industry can reproduce it.

The goal is not to win a single benchmark. The goal is to prove that vertical AI built for crypto performs better over time because it has the right data, tools, and reasoning environment.

Second, I would want CoinStats API to become the default crypto data and research layer for the agent ecosystem. Between MCP, our x402-powered API, and our portfolio intelligence system, any agent that needs crypto context should be able to plug into CoinStats.

Third, I would want CoinStats to go further from research into action. Understanding why something is moving is half the job. Helping users act on those insights safely, inside the same workflow, is the next product frontier.

The end goal has stayed the same. Every crypto holder should have a personal team of analysts working for them 24/7. A year from now, I want CoinStats to be much closer to delivering that experience through CoinStats AI.

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