Library Extract
Contracts, invoices, forms, and reports in — schema-validated structured data out. Every field typed, every extraction traceable to its source passage, and a human review queue for anything below the confidence bar.
What you get
A pipeline, not a demo. Documents arrive by upload, email, or folder watch; they convert to clean text, the model extracts the fields you declared, and the output validates against your schema before it touches any system. High-confidence extractions flow straight to your database or ERP through governed connections; anything below the bar lands in a review queue where a person confirms in seconds with the source passage highlighted.
What makes it different
Template-based tools break on the eleventh vendor's invoice format; raw LLM extraction confidently invents totals. Extract does neither: the model reads like a person, the schema validates like a machine, and a failed validation re-prompts with the specific error instead of shipping bad data. Every extracted value keeps a pointer to the exact passage it came from.
What it can do for you
Turn document piles into data your systems act on, with the audit trail regulated operations need.
- PDF, Word, Excel, and scanned documents to clean structured records
- Your schema, enforced — typed fields, formats, business rules
- Confidence thresholds you set; a review queue for the rest
- Every value traceable to its source passage in the document
- Batch mode for backfills at reduced model cost
- Writes to your database, ERP, or spreadsheet through MCP tools
When it makes sense
A team re-keys documents into a system every week. An intake process stalls on attachments nobody parses. An archive holds years of contracts whose terms exist only as paper. If the fields matter enough to type by hand, they matter enough to extract with validation.
How we ship it
Typical deployment is 2–3 weeks: define the schema on real samples, tune thresholds against a labeled set, wire the destinations, then run the backfill in batch. Accuracy is measured on your documents before go-live, not asserted. Runs in your infrastructure like every Library product.
Questions people actually ask
What happens when the model isn't sure about a field?
It doesn't guess its way into your database. Anything below the confidence bar you set lands in a review queue where a person confirms in seconds, with the source passage highlighted right next to the extracted value. And every output validates against your schema before touching any system — a failed validation re-prompts with the specific error instead of shipping bad data.
Where do our documents and the extracted data actually live?
In your infrastructure, like every Library product. Documents arrive by upload, email, or folder watch; extractions write to your database, ERP, or spreadsheet through governed MCP connections you approve; and every extracted value keeps a pointer to the exact passage it came from, so the audit trail regulated operations need is built in.
How do we know it will be accurate on our documents?
Because we measure it before go-live, not assert it. A typical deployment is 2 to 3 weeks: we define the schema on your real samples, tune confidence thresholds against a labeled set, wire the destinations, then run the backfill in batch mode at reduced model cost. You see the accuracy numbers on your own documents first.
What if our document types multiply after launch?
The schema and pipeline are yours to extend — new document types mean new schemas on the same pipeline, not a new project. Because Extract reads like a person rather than matching templates, the eleventh vendor's invoice format doesn't break it, and when your needs outgrow the packaged shape you graduate to a custom build on the same stack.
Frequently paired with
Related products
Document Q&A done right for regulated, archival, and high-stakes work: multi-document, identity-aware, audit-logged, citation-verified — for legal, M&A, due diligence, research, and compliance teams.
Continuous monitoring of regulated, contractual, or standards documents — alerts when something changes that affects existing workflows, vendors, or compliance posture, with the impact analysis already done.