AI & Data Science Infrastructure

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 Transactions Backfill 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 Pipelines Feed 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 Clustering Index 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 Monitoring Stream 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 State Index 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 streaming Same pipeline definition supports historical backfills and real-time event streams. Switch modes without rebuilding.

Direct database delivery Data lands in PostgreSQL, BigQuery, or S3. No intermediate API layer between your pipeline and your data.

Custom transforms Write TypeScript functions that reshape raw events into your feature schema before storage. Decode, filter, aggregate, enrich.

100+ chains, one schema Normalize cross-chain data into a unified format. Train models across Ethereum, Base, Arbitrum, Solana, and more without chain-specific adapters.

Deterministic replay Re-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

FAQ

Can I train models on historical data and serve inference from the same pipeline?

Yes. The same pipeline can backfill historical ranges for training and keep streaming fresh onchain data for inference, so your feature logic stays consistent across both stages.

Do I control the schema that lands in my feature store or warehouse?

Yes. You define the transformation logic that shapes raw blockchain events into the columns, labels, and derived fields your models expect.

Can Indexing Co unify data across chains before it reaches my ML stack?

Yes. You can normalize EVM and non-EVM sources into one schema before storage, which reduces downstream feature engineering and chain-specific adapters.

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.

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