Deploy document extraction pipelines in minutes
Define your data structure visually or with code. Our AI-powered schema builder helps you create robust extraction templates that handle any document format with precision.
Precision
Define exact field types and validation rules.
Speed
Generate schemas from samples in seconds.
Scale
Handle millions of documents reliably.
End-to-end orchestration for complex pipelines. Build multi-step workflows that parse, split, extract, validate, and route with versioning and durability out of the box.
Flag uncertain extractions for human review. Set confidence thresholds, route edge cases to reviewers, and approve or correct results before they hit your systems.
Describe your document pipeline in natural language. Our agent scaffolds the entire workflow — from ingestion through validation to output — in seconds.
Benchmark extraction accuracy across document types, track drift over time, and ship changes with confidence using built-in evaluation suites.
Quantify extraction certainty with our novel k-LLM consensus approach — run multiple vision language models on the same document and score agreement field-by-field before it reaches your pipeline.
Automatically match each document to the right model tier based on complexity. Optimize cost and accuracy without manual configuration.
Trace every extracted field back to the exact region in the original document. Visual proof that builds trust and simplifies audits.
Lightning-quick deployment
Day 1
Analyze & Structure
Upload documents. AI identifies patterns and generates the optimal data schema.
Day 6
Evaluate & Refine
Build evaluations against your test set. Iterate on edge cases until perfection.
Day 12
Deploy & Scale
Go live with human-in-the-loop validation and scale confidently.
Multiple views, one source of truth.
Review extracted data your way. Edit fields in a form, analyze results in a table, or export structured JSON for your pipeline.
Evals
Deploy in production with confidence
Introducing Evals. A new way to evaluate your pipelines with rigor. Run evals continuously to drive measurable improvements with each iteration.
