Turn documents into data you can trust.
Contracts, claims, statements of work, clinical notes and regulatory filings hold data your teams retype by hand. We build extraction pipelines that read them, pull out the fields you need and check the results.
When you need it.
The hard part isn’t reading the text. It’s handling layouts, tables, scans and edge cases, and knowing when the output is wrong.
- People retype data from PDFsSkilled staff spend hours copying fields from documents into systems.
- Every supplier’s layout is differentTemplates and rules break whenever a document looks slightly different.
- Tables come out scrambledLine items, rates and schedules lose their rows and columns.
- Nobody knows which extractions are wrongErrors are found weeks later, downstream.
How it works.
The shape of what we build, from your data to the people who use it.
- Documents inPDFs, scans, emails
- Layout & OCRPages, tables, reading order
- ExtractThe fields you define
- ValidateRules and confidence checks
- Your systemsStructured data, or a person reviews
What we build.
The parts of a production system, each one tested and handed over.
Layout-aware reading
Models that understand pages, columns, headers and reading order, including scanned documents.
Table extraction
Line items, schedules and rates kept as rows and columns.
Field extraction
The fields you define, returned in a fixed structure your systems can use.
Validation
Checks on types, totals, dates and required fields before anything is saved.
Human review queue
Low-confidence or failed documents routed to a person, with the source page beside the fields.
Accuracy tracking
A labelled test set so you can see accuracy per field and per document type.
What you get.
- An extraction pipeline for your document types
- Structured output with validation rules
- A review screen for documents that need a person
- Accuracy measured per field on your own test set
- Redaction of personal data where your rules require it
How we run it.
Start with a short discovery sprint and decide with real evidence.
- Step 1
Discovery sprint
2 weeks
We map the problem, look at your data and systems, and agree what “good” means as a real eval set.
- Step 2
Build
Working version early
We build the first working version in your environment, with tests and monitoring from the start.
- Step 3
Harden
Before real users
Latency, cost, failure handling and security, then a runbook your team can follow.
- Step 4
Pilot and run
Real users
A pilot with real users and weekly reviews, then ongoing improvement if you want us to stay.
What we build with.
Model- and provider-agnostic: we pick what fits your latency, cost and compliance needs.
- Extraction
- Layout-aware modelsOCRTable extractionStructured output validation
- Models
- OpenAIAnthropicAWS BedrockGoogle Vertex AIAzure OpenAISelf-hosted models
- Safety
- PII detection & redactionOn-premAir-gapped
Frequently asked questions.
Still unsure? A 30-minute call with a tech lead answers the rest.
Ask us directlyWhich documents can you handle?
Typed and scanned PDFs, images and email attachments: contracts, claims, statements of work, clinical notes, regulatory filings and similar.
What happens when the system isn’t sure?
Low-confidence results go to a review queue, with the source page next to the extracted fields, instead of straight into your systems.
How accurate will it be?
We measure it on a labelled sample of your own documents before go-live, per field, so you decide with real numbers.
Can sensitive documents stay inside our network?
Yes. We can redact personal data and deploy on-prem or air-gapped when the data can’t leave.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
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