Pipelines that turn messy documents into usable data
Scanned contracts, faxed forms, supplier invoices in forty layouts, attachments that arrive by email at 3am. We build the pipeline that ingests them, extracts what matters and flags what it is unsure about.
When a pipeline beats more people
Volume outgrew manual entry
A team is retyping documents into a system. We automate the predictable 80% and route the rest to a review queue that is quick to work through.
Every source has a different format
Classification first, extraction second. New layouts become configuration rather than a code change.
Silent errors are unacceptable
Confidence scores, validation rules and a human step for anything below threshold. Wrong data never lands quietly in the system of record.
A document pipeline is judged on its worst day: the rotated scan, the merged pages, the invoice with two totals. We design for those first.
Classify, then extract
Trying to extract before knowing what a document is produces confident garbage. We split the pipeline into stages with their own accuracy targets, so a drop in quality can be traced to a stage instead of to “the AI”.
Each stage is replayable. Reprocessing a month of documents after a rule change is a routine job, not an incident.
Humans stay in the loop where it pays
The review interface is part of the product, not an afterthought: keyboard-first, showing the page region a value came from, and correcting the record and the training data in one action.
Corrections are measured. If a field needs review 30% of the time, that is a number on a dashboard with a name attached to improving it.
The goal is not zero human review. It is knowing exactly which documents deserve it.
Work like this usually touches
Drowning in documents that should be data?
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