Turn Paper Into Data.
Businesses drown in documents that hold the data they need in a shape no system can read. Extraction turns the scan, the statement and the emailed PDF into fields, with a note on how sure it is.
How this actually works.
- 01
Classify, then extract
The document is identified first, then read with a schema that fits its type. A bank statement and a bill of lading are not parsed the same way.
- 02
Scans and photographs included
OCR for the documents that arrive as a picture of a page, which in practice is most of them.
- 03
Confidence on every field
Each value carries a score, so low-confidence fields go to a person and the rest flow through untouched.
- 04
It writes into the system of record
Extraction that ends in a JSON file has moved the problem. The output lands in the CRM, the ledger or the pipeline where the work continues.
Industries this comes up in most.
Questions people ask.
What comes up first when someone is deciding about document ai and extraction.
How accurate is it?
Accurate enough to be worth measuring, which is why every field carries a confidence score and a review queue exists. We benchmark on your own documents before go-live rather than quoting a number from a vendor page.
What happens to sensitive documents?
They stay in your storage, in your cloud account. We scope retention and access before anything is processed, and nothing is used to train a model.
Let’s Build What’s Next.
Bring the business problem. We’ll talk through what would make a difference and where to start.