Coinstats Ceo Narek Gevorgyan On Building A Crypto Ai Agent For Real-Time Research

What Happened

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.

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.

Market Context

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.

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.

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.

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.

Why It Matters

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 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?

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.

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

Details

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.

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.

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.

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.

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

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.