Datachain

10× AI for Financial Documents, Without Reprocessing on Every Retry

Bring Claude Code, Cursor, and Codex to financial analysis. Add data context, checkpoint recovery, and delta updates—without re-reading 1M documents on every retry.

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Trusted by startups and Fortune 500 companies

  • DeGould
  • Kibsi
  • Hugging Face
  • Mantis
  • Motorway
  • Inlab
  • Pieces
  • Sicara
  • UBS
  • SumerSports
  • UK Hydrographic Office
  • XP Inc.
  • Cyclica
  • Aicon
  • Billie
  • Papercup

Give your agents\nthe missing data context

Claude Code, Cursor, and Codex read your code, not your storage. They don’t know your 10-K counts, datasets, or what’s already extracted. DataChain does not replace them. Our skill and MCP add typed datasets, lineage, checkpoint recovery, and delta updates that process only changes—without paying for the same work twice.

Financial Documents, Reusable Results

Resilience

Never reprocess what already succeeded

Every pipeline runs as checkpointed stages. Resume from the exact stage of failure, and use delta updates to process only what changed.

  • Keep the work already done: no re-extraction of the 800K documents that succeeded.
  • Add 1,000 new filings to a 1M-document corpus by processing only the new ones.
  • Measured: a 2.7× lower cost-of-failure ratio. Failed runs cost 2.5× as much as successful ones with vanilla Claude Code, versus 0.91× on average with DataChain.

Brain

Storage context the agent reads first

Agents read existing data and results before processing what’s missing.

  • Knowledge Base catalogs file counts, dataset memberships, schemas, lineage, and prior LLM summaries. Dataset DB serves typed records at warehouse speed.
  • Bring 10-Ks, contracts, disclosures, and due-diligence packs under one Pydantic schema.
  • Reuse extracted covenants, risk factors, and segment financials as decision assets—with version, lineage, and author on every materialized result.
  • 100,000,000× cheaper to recall than to recompute, measured on a 1,500-document SEC 10-K corpus.

Hands

Distributed execution from inside the chat

Run LLM extraction and embedding across your VPC, directly from chat.

  • Attach CPU, GPU, and high-memory pools; scale from 1 to 1000+ machines via the SDK, with no extra framework.
  • Run 300 CPUs or 10 T4 GPUs on GCP, or 4 A100 GPUs on Nebius.
  • DataChain manages your clusters: workers start on demand and shut down when idle.
  • Bytes stay in your storage. BYOC keeps compute in your AWS, GCP, or Azure account, behind your VPC and existing IAM.

Research-measured proof

Two 2026 papers measure DataChain as a Claude Code extension on SEC 10-K filings—the financial documents your team works with.

  • 2.7× lower cost-of-failure ratio

    Across measured runs, checkpoint recovery reduces the cost-of-failure ratio compared with vanilla Claude Code.

  • 8.4× faster follow-up questions

    The next question costs 3.4× less. Across five sessions, reuse compounds into 5× savings.

  • +40 pp higher pass rate

    On reuse-rich tasks, DataChain improves the pass rate by 40 percentage points, helping agents build on prior work.

Add the missing data context layer to your object storage

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