Document data extraction that turns any file into structured fields

Why template-based extraction keeps failing

The old approach to data extraction was templates: define where each field sits on the page, and read from those coordinates. It works beautifully for exactly one layout and falls apart the moment reality intrudes. A new supplier, a redesigned form, a scan that is slightly rotated, a value that wraps onto a second line, and the template misses or grabs the wrong text. Teams end up maintaining a growing library of brittle templates and still fall back to manual entry for anything unusual.

Language models changed the economics of this. Instead of reading from a fixed position, the model reads the document and understands what each value means, so it finds the invoice total, the account number, or the test result wherever it appears and however it is labelled. That means one extraction setup handles documents from many senders and survives layout changes without a rebuild. It also handles the messy middle: values expressed in different formats, mixed languages, fields that are sometimes present and sometimes not.

• Templates break on new layouts, redesigns, and rotated scans
• Maintaining a template library is its own growing burden
• A model reads meaning, so one setup spans many senders and formats
• Handles mixed languages, variable formats, and optional fields

Extract, structure, validate, deliver

You tell us the fields you need and the shape you want them in, and we build a pipeline that produces exactly that. The document comes in through whatever channel suits you, OCR handles scans and images while digital files are parsed natively, and the model extracts your fields into clean, typed, structured data: dates as dates, amounts as numbers, identifiers normalised to your format. The output is not a rough text dump, it is data your systems can consume directly.

Before it is delivered, it is validated. We check formats, ranges, and internal consistency, cross-reference against your existing records where that is possible, and score the model's confidence per field. Values that pass flow straight into the destination system, database, spreadsheet, CRM, or ERP, through its API. Values that fail a check or come back low-confidence are routed to a person to confirm. You get structured data you can trust, not a fast extraction you then have to audit by hand.

• You define the fields and the exact output shape
• OCR for scans, native parsing for digital files, one pipeline
• Typed, normalised output: dates, numbers, and IDs in your format
• Per-field confidence scoring and validation before delivery
• Clean data written into your database, sheet, CRM, or ERP

Built for the document types you actually handle

There is no generic extractor that is good at everything, so we build for your reality. In the free assessment we look at the document types you deal with, how varied they are, how clean they arrive, and how much a wrong field costs, and we design the pipeline around that. A high-volume, consistent document type can run with light-touch review; a rare or high-stakes one gets tighter validation and more human oversight. We would rather scope this honestly than promise a single model that reads everything perfectly.

That honesty extends to telling you when a document type is not worth automating. If something arrives a handful of times a month in wildly inconsistent forms, the effort to build and maintain reliable extraction may never pay back, and manual handling stays the right answer. We will say so. When we do build, everything runs in your accounts, the field definitions and validation rules are documented, and you own the pipeline outright, so you can extend it to new document types yourself over time.

Your pipeline, your data, no lock-in

Extraction pipelines see a lot of your data, so where they run and who controls them matters. Ours run on your infrastructure and accounts, through the LLM setup you are comfortable with, whether that is a commercial API under business terms or a self-hosted model for data-residency reasons. The extraction logic, prompts, field definitions, and validation rules are yours, documented and readable, so your team or a future partner can maintain and extend them. And because every extraction is logged with its confidence and any human correction, you always have an audit trail of what was pulled from which document and why.