Contracts read themselves

Extraction is the easy half. Azure will pull fields off an invoice on day one. The half that decides whether anyone actually uses the system is what happens to a field the model is not sure about, and whether a human ever finds out.

Talk about your document pile See how the pipeline works

The pile does not shrink on its own. It just moves to whoever complains least.

Document AI · corporate operations
20 hrs/week 2 hrs
−90%

Document processing per week, at 95%+ extraction accuracy across invoices, contracts and forms.

The part nobody demos

Any model can read an invoice. Trusting it is the project.

A demo runs on a clean PDF. Your archive is scans, phone photographs, three supplier layouts that changed last year, and a handful of documents nobody can classify. A model that is right 88% of the time and silent about the other 12% is worse than no model, because now the errors are inside your data.

So we build the validation around the extraction: rules that know what your documents should look like, a confidence threshold that routes the doubtful ones to a person, and a record of what was corrected. That is how a pipeline reaches 95%+ accuracy and stays there once the layouts drift.

The four steps Then ask questions of it
Not

A model call and a hope

Extraction without validation moves the error from the typist to the database, where it is much harder to find.

Not

A tool your team has to babysit

If a person checks every field, nothing was automated. The threshold exists so people see the 8% that needs them, not the 92% that does not.

Is

A pipeline that reports on itself

Which document types fail, which fields keep needing a correction, and whether accuracy moved this month. Measurable, therefore fixable.

Four steps, and the third is the one that matters. Everyone ships the first two.

Built on Azure Document Intelligence and Azure AI Search, inside your own Azure subscription. You own the pipeline and the data; nothing routes through a layer we hold.

01

Read

Azure Document Intelligence over invoices, contracts, forms and scans, including the ones that arrive as photographs of paper.

02

Validate

Your rules, applied to every field. Totals that must add up, suppliers that must exist, dates that must be plausible, VAT that must resolve.

03

Route the doubt

Low-confidence fields go to a person with the source page open beside them, rather than silently into the database. Every correction is recorded.

04

Search and ask

Azure AI Search over the extracted set, so the archive becomes something you can query rather than something you store.

Step 04 is where this offer meets the other one. Once documents are structured, an MCP server can put them inside the assistant your team already uses, so people ask questions of the archive instead of searching it.

Thousands of real documents. Not a pilot, not a sample set.

The accuracy figure is measured on production traffic, including the difficult documents, because a number measured on clean inputs tells you nothing about a Tuesday.

Corporate operations

Invoices, contracts and forms

Document processing across a corporate operations team fell from 20 hours a week to 2, at 95%+ extraction accuracy. Same documents, same people, different Tuesday.

Legal

Case law and regulations

Full-text search over a million court decisions, feeding the assistant layer a European law firm uses daily.

Development finance

Facility agreements

Obligations and covenants extracted from signed agreements, reconciled against the live configuration, and committed only on an operator's sign-off.

What people ask before they say yes. Answered plainly.

What accuracy can we actually expect?
95%+ on the field set that matters, measured across thousands of real production documents rather than a clean sample. The number is a property of the validation layer as much as the model: rules catch what extraction gets wrong, and low-confidence fields are routed to a person instead of being accepted silently.
What happens to a field the model is unsure about?
It goes to a reviewer with the source page open beside it, not into the database. A confidence threshold decides which fields qualify, and every correction is recorded, so you can see which document types and which fields keep needing a human.
Does it handle scans and photographs of paper?
Yes. Invoices, contracts, forms and scans, including documents that arrive as photographs taken on a phone. Quality varies, which is exactly why the validation and confidence-routing layer exists rather than a straight model call.
Where does the data go afterwards?
Into your own systems, in your Azure subscription. From there Azure AI Search makes the archive queryable, and an MCP server can put the extracted set inside the assistant your team already uses.
Is this the same thing as an AI assistant over our data?
No, they are two offers and they often run together. Document AI turns paper and PDFs into structured data. An MCP server lets an assistant ask questions of live systems. Document AI is frequently the step that produces the data the assistant then answers from.

Send us ten documents, including the three worst ones. In half an hour we can tell you what a pipeline would get right, what it would flag, and roughly what the remaining manual work looks like.

Sometimes the answer is that the documents need to arrive differently before any model helps. We would rather say so than sell you a pipeline over a process problem.

Related

MCP servers & AI assistants

Once the documents are structured, put them inside the assistant your team already uses. Five servers in production, 200+ governed tools.

All AI work

How the AI offers fit together, and which one your problem actually is.

Case studies

Named clients and the outcomes we can publish.