Turn the document backlog into structured data your team can act on.
We build OCR-plus-LLM pipelines that extract typed fields from scanned forms, contracts, and operational paperwork — with schema validation, human review where it matters, and an evidence trail your auditor can read.
What you get
Three concrete deliverables.
Document classification and extraction pipeline
Per-template parsers, schema-validated outputs, and confidence scoring so downstream systems consume clean, typed records.
Human-in-the-loop review console
Reviewer queue, field-level confidence highlighting, side-by-side source-and-extraction view, and an audit trail of every correction.
Evidence and audit package
Source document, extracted record, reviewer trail, and model version stamped on every output — examiner-ready in a single export.
How we work
From kickoff to production.
Document inventory and target schemas
Catalog the document types in scope, score them by volume and complexity, and agree on the typed schema each will produce.
Labeling and gold-set creation
Build a labeled gold set per document type with your reviewers. This is the eval bench every later change runs against.
Pipeline and review console build
Wire OCR, classification, extraction, validation, and the reviewer console. Confidence thresholds tuned on the gold set, not on vibes.
Production rollout and improvement
Roll out by document type behind a feature flag, monitor extraction accuracy and reviewer override rates, and retrain when the curve plateaus.
The stack we build on.
Cloud-agnostic. We meet you where your tenant lives.
Outcome metrics
Median across deployed document types
Versus pre-pipeline baseline
Post-stabilization, gold-set tuned
From the field
One we shipped.
Regulated lender · loan operations
Replaced a manual document indexing team with an extraction pipeline plus a reviewer queue. Throughput up 12x, error rate cut in half, full audit trail on every loan file.
Documents per reviewer
Vs. fully manual baseline
FAQ
Questions buyers ask first.
How do you handle documents the model has not seen before?
What about handwriting, stamps, and signature pages?
How do you measure extraction quality?
Where do source documents live?
What evidence ships with each extracted record?
Related services
What buyers usually pair with this.
RAG and knowledge systems
Make the extracted records and the source documents searchable with citation in the same query layer.
See the serviceAgentic systems
Wire the extracted records into multi-step workflows that close the loop on operations work.
See the serviceAI engineering
Production inference, evaluation harness, and on-call rotation for the extraction models themselves.
See the service