Analyze Wallets With AI
AI profiling of 60 candidate wallets cut a week of manual review to an afternoon and identified 4 worth monitoring.
/ quick answer
A researcher needed to profile 60 addresses to find genuinely skilled operators, with each manual review taking around 40 minutes. AI profiling of 60 candidate wallets cut a week of manual review to an afternoon and identified 4 worth monitoring.
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What is the Analyze Wallets With AI use case?
A researcher needed to profile 60 addresses to find genuinely skilled operators, with each manual review taking around 40 minutes.
What was the outcome?
Profiling 60 wallets took about 5 hours instead of an estimated 40. 38 were classified as bots or market makers and excluded immediately; 4 showed broad-based profitability across two market phases and entered the watchlist with size filters.
Which tools were used?
Indexer API — bulk transaction exports per address, Spreadsheet normalisation — spam and self-transfer filtering, Claude with the wallet analysis prompt — classification and PnL reasoning with shown calculations, Explorer — verification of decisive numbers.
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Related concepts
The vocabulary this page depends on.
- →On-chain Data
On-chain data is the public record of every transaction, balance and contract call — the raw material for wallet tracking and market research.
- →Deep Research (AI)
Long-running AI research task that produces a cited multi-page report.
- →Research Automation
Research automation turns a question into a sourced, structured answer using search, retrieval, extraction and synthesis agents.
Related workflows
Turn this into a repeatable process.
- →Analyze A Wallet With AI
Turn a raw transaction history into a readable profile: strategy, holding periods, risk behaviour and realised performance.
- →Smart Money Tracking Workflow
Build a curated wallet watchlist, monitor it for meaningful trades, and use alerts as research triggers rather than buy signals.
- →AI Crypto Research Workflow
A repeatable research loop: turn a question into market data, on-chain evidence and a written risk view before any position is considered.
- →AI Token Research Workflow
Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.
Related tool stacks
The tools that run it in production.
- →AI Crypto Research Stack
A read-only research stack combining an AI assistant, web search, market data and on-chain analytics to screen assets quickly.
- →On-chain Research Stack
Explorer, indexer and analytics layers combined so wallet and token questions get answered with verifiable data.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
- →Research Automation Stack
Search, fetch, extract and synthesise sourced briefs on a schedule.
Related prompts
Reusable prompts for this job.
- →Token Research Prompt
Structures a full token due-diligence pass: mechanics, liquidity, concentration, bear case and unverifiable claims flagged explicitly.
- →Crypto Research Agent Prompt
System prompt for a research agent that must cite sources, separate fact from inference, and refuse to predict prices.
- →Wallet Analysis Prompt
Turns a transaction export into a behavioural profile: strategy type, timeframes, risk pattern and whether the wallet is worth watching.
- →Smart Money Analysis Prompt
Tests whether a group of 'smart money' wallets is genuinely informative or a survivorship-biased label.
Related use cases
How people apply it, and what came out.
- →Research A Token With AI
A structured AI research pass cut token screening from three hours to 35 minutes and produced documented passes instead of impulse entries.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
- →Tech Creator Replaces a Research Assistant With a Workflow
A YouTuber cut their research time per video from 8 hours to 90 minutes.
- →Find Smart Money Activity
A research group verified 40 leaderboard wallets and kept only 11, turning a noisy signal source into a usable research queue.
Comparisons & alternatives
Pick between the options.
- →AI Research vs Traditional Research
AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.
- →On-chain Data vs Exchange Data
On-chain data shows verifiable wallet-level behaviour; exchange data shows aggregate price discovery. Serious research needs both.