On-chain Research Stack

On-chain data is the public record of every transaction, balance and contract call — the raw material for wallet tracking and market research.

Crypto automation is rule-based execution of monitoring, alerting and recurring on-chain actions, so decisions are made once and applied consistently.

A gas fee is the network payment for computation and storage in a transaction, priced by demand rather than by trade size.

Smart money is a label for wallets with a documented history of profitable, early positioning — a research filter, not a signal to copy blindly.

Turn a raw transaction history into a readable profile: strategy, holding periods, risk behaviour and realised performance.

Watch the pools you depend on for exit liquidity, so a position becomes unexitable only in theory, not by surprise.

Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.

A repeatable research loop: turn a question into market data, on-chain evidence and a written risk view before any position is considered.

A read-only research stack combining an AI assistant, web search, market data and on-chain analytics to screen assets quickly.

Search, fetch, extract and synthesise sourced briefs on a schedule.

Automation platform, data APIs, alerting and optional wallet execution — the operational layer for monitoring and recurring actions.

Default toolset for analysts, founders and creators doing deep research with AI.

Structures a full token due-diligence pass: mechanics, liquidity, concentration, bear case and unverifiable claims flagged explicitly.

Runs a pre-mortem on a position or protocol: enumerates failure modes, likelihood, impact and observable early warnings.

Turns a transaction export into a behavioural profile: strategy type, timeframes, risk pattern and whether the wallet is worth watching.

Explains what a specific transaction did, what it authorised, and what risk it left behind.

A research group verified 40 leaderboard wallets and kept only 11, turning a noisy signal source into a usable research queue.

Depth monitoring across 9 positions flagged three tokens as effectively unexitable at their current size, forcing a resize before it mattered.

A structured AI research pass cut token screening from three hours to 35 minutes and produced documented passes instead of impulse entries.

A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.

On-chain data shows verifiable wallet-level behaviour; exchange data shows aggregate price discovery. Serious research needs both.

AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.