Solutions · Data Pipelines

Pipelines that
reason, not just run.

Extract from messy sources, clean and enrich with LLM steps, and route results downstream — on a durable engine with retries, scheduling, and full lineage on every record.

Build a pipeline → See the guide
Extract

From anywhere

Pull from APIs, databases, PDFs, scraped pages, and inboxes. Agents handle the formats rules-based ETL chokes on.

Transform

Clean & enrich

Normalize, dedupe, classify, and enrich records with LLM reasoning — validated against a schema before they pass.

Load

Route downstream

Write to your warehouse, CRM, or queue. Branch on content, and fan out to multiple sinks in parallel.

Production-grade by default

The boring parts, handled

  • Scheduling & backfills with cron, events, and idempotency.
  • Retries & dead-letter so one bad record doesn't fail the batch.
  • Per-record lineage — trace any output back to its source.
  • Cost caps per run so large batches stay predictable.
pipeline enrich_leads {
  source    postgres."raw_leads"
  step      classify_industry()
  step      enrich_from_web()
  validate  schema(Lead)
  sink      snowflake."leads"
  schedule  "every 1h"
}

Give your data team an agentic ETL

Build a pipeline that handles the messy 20% rules-based jobs can't.

Get started → See observability