Structured blockchain data for ML training, real-time inference, and on-chain monitoring: batch and streaming pipelines across 100+ chains.
Machine learning models trained on blockchain data need clean inputs. Not raw hex from RPC nodes. Not rate-limited API responses. Structured, typed, queryable data that maps to your feature schema.
Indexing Co delivers blockchain data in the format data science teams actually use. Stream real-time events into your feature store. Backfill years of transaction history for model training. Push structured outputs to BigQuery, PostgreSQL, or S3, wherever your training pipeline reads from.
Whether you're building fraud detection models, wallet clustering algorithms, or on-chain risk scoring, the data layer starts here.
Use Cases
ML Training on Historical TransactionsBackfill millions of labeled transactions across 100+ chains into your training environment. Define the event types, token contracts, and address sets you care about. Get structured rows, not raw logs. Train models on swap patterns, transfer volumes, gas usage, or any on-chain signal your features require.
Real-Time Inference PipelinesFeed live blockchain events into your inference endpoint. New swap on Uniswap, new transfer to a flagged wallet, new contract deployment: your model scores it within seconds of the on-chain event. sub-500ms (dedicated infra) latency from block to your pipeline.
Wallet Behavior ClusteringIndex transaction histories for millions of wallets. Build behavioral profiles based on protocol usage, token holdings, transaction frequency, and interaction patterns. Used by compliance teams, marketing platforms, and identity protocols.
Anomaly Detection and MonitoringStream on-chain events through your anomaly detection models in real time. Flag unusual transfer patterns, sudden liquidity removals, or abnormal gas spikes. Deliver alerts via webhooks to your monitoring stack.
Vector Search Over Blockchain StateIndex contract state, token metadata, and transaction context into vector-compatible formats. Query semantic similarity across on-chain entities. Power recommendation engines, search interfaces, and agent retrieval systems.
Why Indexing Co for Data Science Teams
Batch and streamingSame pipeline definition supports historical backfills and real-time event streams. Switch modes without rebuilding.
Direct database deliveryData lands in PostgreSQL, BigQuery, or S3. No intermediate API layer between your pipeline and your data.
Custom transformsWrite TypeScript functions that reshape raw events into your feature schema before storage. Decode, filter, aggregate, enrich.
100+ chains, one schemaNormalize cross-chain data into a unified format. Train models across Ethereum, Base, Arbitrum, Solana, and more without chain-specific adapters.
Deterministic replayRe-run any historical range through updated transforms. Reproduce training datasets exactly.
Key Numbers
100+ chains indexed in parallel
1B+ events/day processed across all pipelines
sub-500ms block-to-database on dedicated infrastructure
1.6 TB/day of raw blockchain data ingested
Years of history available for backfill on major chains
Get Started
Set up a data pipeline that feeds structured blockchain events into your ML infrastructure. Define your sources, write your transforms, pick your delivery target.