Document processing
Somewhere in your business, a person is reading a PDF and typing what it says into a form. Possibly right now.
The most boring high-value problem you have
It's rarely one person's whole job, which is why it stays invisible. It's forty minutes here, a Thursday afternoon there, spread across a team, absorbed into everyone's week.
Add it up and it's frequently the largest recoverable block of time in an organisation. It's also the most measurable AI project available: you know how many documents, you know how long each takes, and you can compare before and after without a debate about attribution.
That combination — clear baseline, clear result, contained scope — makes it the project we most often recommend to a team doing this for the first time.
What we build
- Invoice and receipt processing
- Line items, totals, tax, supplier matching, and validation against purchase orders.
- Contract analysis
- Key terms, dates, obligations, renewal windows, and non-standard clauses flagged for review.
- Forms and applications
- Structured data out of submitted forms, however they arrived: typed, scanned, or photographed at an angle in poor light.
- Reports and statements
- Extracting figures from documents that arrive as PDFs and need to become rows.
- Classification and routing
- Sorting mixed inbound documents by type and sending each to the right process.
How we build it
- We start with your worst documents
- Not the clean template — the scanned copy of a fax, the phone photo, the one with handwriting in the margin. If the system handles those, the good ones are trivial. Building against clean samples first is how these projects end up at 70% accuracy in production.
- Confidence scores on every field
- The system knows which extractions it's sure about. High confidence flows straight through; low confidence goes to a person. You set the threshold, and you can move it as trust builds.
- Validation against what you already know
- Extracted values checked against your existing records — does this supplier exist, does the total match the line items, is this date plausible. Catches errors before they reach your systems.
- A review interface for the exceptions
- Fast to work through, showing the document and the extraction side by side. The goal isn't zero human involvement; it's turning forty minutes of typing into thirty seconds of confirming.
- Corrections improve the system
- Every human fix is signal. We build the loop so accuracy climbs rather than plateaus.
Measuring it
We'll establish the baseline before building: documents per month, minutes each, error rate now. Then the same numbers after. It's rare to get a comparison this clean, so we make the most of it.
Before you ask.
Sometimes, depending on quality. Send us samples and we'll tell you honestly rather than optimistically.
Workflow Automation
Your systems are fine. It's the space between them where the week disappears.
AI & MLComputer Vision
The model works on the sample images. The question is whether it works in the car park, at six in the evening, in the rain.
AI & MLNatural Language Processing
Your customers don't write like your training data. They abbreviate, misspell, switch languages mid-sentence, and describe your product using words you've never used for it.
Tell us what you're trying to build.
A 30-minute call, no charge and no pitch deck. Describe the problem and we'll tell you how we'd approach it, roughly what it costs, and whether we're the right team for it. If we're not, we'll say so.
30 minutes · No charge · No deck