Extraction from any layout
Pull the fields you care about out of PDFs, scans, and photos — including documents that never follow the same template twice.
AI document processing that turns days of review into minutes.
We build AI systems that read the documents your business runs on — invoices, contracts, filings, tenders — and hand back clean, structured data your team can act on. A transparent process: we scope the work with you, agree the outcome and the price before we start, and tell you the moment anything threatens the estimate. Working prototype on your data in 2–4 weeks.
The challenge
Most of what your business knows is trapped in documents.
The information that decides your quotes, your invoices, your risk, and your bids arrives as PDFs, scans, and email attachments. None of it is in a database. Someone opens each file, reads it, and re-keys the parts that matter.
That work is paid time, and it scales with your document volume, not with your revenue. It also fails quietly: a number typed into the wrong column on a Friday afternoon becomes a decision nobody can trace back.
The bottleneck is rarely the software you already own. It's the gap between a document arriving and the data inside it becoming usable — and that gap is now something AI can close reliably.
Why now. Automated document extraction has crossed the threshold where it holds up on real, messy files — not just clean templates. Every month of reading documents by hand is a cost you don't get back.
What we build
From an inbox full of files to data you can query.
Every system we build is assembled from the same set of building blocks. Which ones you need depends on your documents — we work that out with you before anyone writes code.
Classification and routing
Sort incoming files by type and send each one down the right path automatically, so nothing sits in a shared inbox waiting to be noticed.
Validation and cross-checks
Totals that don't add up, dates out of range, a figure that contradicts page 40 — flagged before the data reaches your systems.
Human review where it counts
Low-confidence results go to a person, not straight through. Your team reviews the exceptions instead of reading everything.
Structured output, your format
Excel, CSV, a database, or a direct push into the tool you already use. The output is designed around what happens next, not around our pipeline.
Integration with your systems
Results land in your ERP, CRM, or accounting software through an API — no second place to check, no copy-paste step at the end.
Use cases
By document type.
Most projects start with one document type — the one that eats the most of your team's week. Here is where automated document extraction pays back fastest.
Invoices and receipts
Line items, totals, tax, supplier, dates and payment terms extracted and matched against purchase orders. Exceptions get flagged; the rest posts straight through.
Contracts and agreements
Parties, term, renewal and notice dates, liability caps, payment terms — pulled into one table so nothing auto-renews because a clause sat unread in a folder.
Financial filings and reports
Figures pulled out of statements and regulatory filings across every statement type, exported to Excel for the analysis your team actually wants to spend its time on.
Tenders and RFPs
Requirements scattered across dozens of attachments, consolidated into one structured audit with a clear go/no-go read — before you sink days into a bid.
Two situations we see most often
PDF data extraction at volume
You have thousands of PDFs and a script that half-works — it handles the clean files and quietly mangles the rest. We build extraction that copes with real-world documents: scanned pages, rotated tables, multi-column layouts, headers that shift between issuers. Confidence scores come with every field, so you know which results deserve a second look.
Accounting firms and agencies
When document handling is the service you sell, throughput is margin. We automate the intake side — client documents in, structured data out — so the same team covers more clients without more overtime. Per-client rules and formats are part of the design, not a workaround bolted on later.
Bring one document type. We'll show you what AI does with it — on your files.
Show me on my dataHow we work
From first call to a system in production.
No long discovery documents before you see anything working. You get a prototype on your own documents early enough to change your mind about the scope.
- Week 1
Discovery
We map the goal, look at real samples of your documents, and agree on what "done" means in numbers.
- Weeks 1–3
Build the pipeline
Extraction, validation, and output built in short sprints, with demos on your own files as we go.
- Weeks 2–4
Measure and tune
We score accuracy against a set you sign off on, then tune the weak spots until the numbers hold.
- Launch
Launch and support
We deploy, integrate with your systems, monitor, and keep supporting it — the system keeps running after handover.
Test-driven development and security checks run through all of it, not as a phase at the end.
From our work
A document pipeline we shipped.
Financial-statement extraction
SEC filings, structured automatically
- Challenge
- SEC 10-K/10-Q filings whose figures had to be pulled out by hand, statement type by statement type, before any analysis could start.
- Solution
- An extraction pipeline covering all five SEC statement types, with validation and Excel export built in.
- Result
- Up to 98% extraction accuracy across all five statement types.
We also run BidReview, a production AI system for tender and RFP documents — the same extraction work applied to the hardest document set our clients face. Tender & RFP analysis →
Engagement
Pick the engagement that fits the job.
Document projects differ wildly in how well-defined they are on day one. Rather than force one commercial model onto all of them, we route the work to the shape that fits.
Fixed scope
For well-defined tasks — a prototype, one document type, one pipeline. We agree on the outcome upfront and estimate each feature before we build it.
Discovery first
For bigger or fuzzier projects: a paid Discovery (3–5 days, $1,500–3,000, fixed price agreed upfront) → artefacts, plan, and a priced proposal. You keep the artefacts either way.
Dedicated capacity
For ongoing document work: a monthly retainer of reserved days for new document types, features, and support — how we run our longest engagement today.
On pricing: we estimate each feature upfront and do our best to stay within 20% of the estimate. If something turns out bigger than we thought, you hear it when we find out, not on the invoice.
FAQ
Common questions.
How accurate is AI document processing, really?
It depends on the document type and how clean your source files are. On a structured-filing project we reached up to 98% extraction accuracy across all five statement types. We measure accuracy on a sample you approve before launch, and we tell you the number rather than quoting a marketing figure.
How is this different from OCR?
OCR turns pixels into characters. It tells you the page says "Total 4,180.00" but not that this is the net total for invoice 118 from a supplier under a contract expiring in March. AI document processing adds that understanding — it finds the right fields in documents that don't share a layout, and cross-checks them against each other.
Our documents don't follow a fixed template. Does that break it?
That's the normal case, and it's exactly where template-based tools fall over. Our systems work from what a field means rather than where it sits on the page, so a new supplier's invoice layout doesn't require a rebuild.
How long until we see something working?
A working prototype on your own data in 2–4 weeks. Discovery is week one, and you see demos on your real documents during the build rather than at the end.
What happens to our documents? Are they sent to a third party?
That's your call, and we design for it. We can run models in your own cloud account or entirely inside your network, so documents never leave your perimeter. If a hosted model is acceptable, we say clearly which provider handles what and under which data terms.
Can it plug into the systems we already use?
Yes — that's usually the point. Results go into your ERP, CRM, accounting software, or database through an API, so the output shows up where your team already works instead of in a new tool nobody opens.
What does it cost to run once it's live?
Two parts: infrastructure and model usage, which scale with document volume, and support. We size both during discovery so you see the per-document cost before committing, not after.
What if the volume is small — is it still worth it?
Sometimes not, and we'll say so. If a person spends two hours a month on a document type, automating it is a hobby project. The honest test is whether the time saved and the errors avoided outweigh the build and running costs — we run that arithmetic with you before proposing anything.
Next step
Let's look at your documents.
Tell us the one document type that eats the most of your team's time. We'll come back with what AI can do with it — on your data, not on a demo file.