→ 01
Structured data out, every time
Documents go in; clean, schema-conforming records come out — with confidence scores so you know what to double-check.
02/DOCUMENT AUTOMATION
THE PROBLEM You're paying someone — or worse, several someones — to read PDFs, invoices, contracts, or forms and copy fields into spreadsheets and databases.
Stop retyping what's already on the page.
We build pipelines that turn unstructured documents into clean structured data: invoice amounts into your accounting system, contract terms into your CRM, claim fields into your case manager. AI for the messy bits, deterministic code for the parts that have to be exact, and human review where the cost of a wrong answer warrants it.
→/OUTCOMES
→ 02
Hours back per week
Free up the people doing the typing for the work only they can do.
→ 03
Human-in-the-loop where it matters
Low-confidence extractions route to a reviewer; high-confidence ones flow straight through.
→ 04
Audit trail
Every record links back to the source document and the exact extraction model and version. No magic.
→/TOOLING
A representative — not exhaustive — set of tools we reach for on document automation engagements. We pick by fit, not by brand loyalty.
- Claude vision
- OpenAI structured outputs
- Tesseract / AWS Textract
- Python / TypeScript
- PostgreSQL
- S3 / Google Cloud Storage
- Zod / Pydantic schema validation
→/PROCESS
STEP 01
Sample the documents
We collect a representative sample — including the weird ones — and define the target schema together.
STEP 02
Build the pipeline
Ingestion, OCR if needed, structured extraction with confidence scoring, schema validation, and downstream write-back.
STEP 03
Review-and-approve UI
If accuracy matters, a small admin tool surfaces low-confidence rows for a human to confirm.
STEP 04
Hand-off
Code, model versions, and a runbook in your repo. Schema changes are a config edit.
→/FAQ
How accurate is it?
Depends on the document and the field. We measure on real samples and tell you the number — typically 90–99% on routine fields, with confidence scoring on the rest so a human can review.
What if our document layouts change?
Modern vision models tolerate layout shifts well. We rerun the eval set on changes and tune as needed.
Do we send our data to a third-party LLM?
We design around your privacy needs — Claude/OpenAI APIs with no-train provisions, on-device models, or self-hosted options. We pick what fits.