Documents in, structured records out, exceptions to a person.

Turn invoices, contracts and forms into the fields your ERP, CRM or case system needs. The agent checks the extracted values against your rules and sends exceptions to a person with the source page attached.

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Who it is for

Teams that need this workflow.

Finance teams keying invoices and receipts into an ERP by hand

Legal and property teams abstracting contracts and leases into a register

Public programs processing applications that carry the same twenty fields in a hundred layouts

What it does

Document processing agents, by the job.

Read

Reads your document layouts

Scans, photos, PDFs and email attachments are tested in the layouts your suppliers send. The evaluation set checks whether the model reads those pages reliably.

Extract

Extracts and validates

Fields checked against each other and against your master data: totals that add up, vendor numbers that exist, dates in range. A failed check is an exception, never a silent write.

Write

Writes to the system of record

The record lands in your ERP, CRM or case system through its API, with the source page and the confidence per field stored beside it.

Escalate

Escalates with the evidence

A person sees the page, the extracted values and the check that failed, resolves it in one screen, and the resolution teaches the rule set.

How it is built

The build, in order.

The pilot covers one defined workflow. We agree its success metric and kill threshold in writing before the build; the steps below show how this system is developed and evaluated.

Label a sample first

A sample of your real documents, hand-labelled with your team, becomes the evaluation set every later change is measured against. It is written before the build starts.

Report accuracy per field

Extraction is measured field by field against that set before any time-saving claim is made. A field the model cannot read reliably is routed to a person by design.

Shadow the current process

The agent processes live documents beside your team for a cycle. Its records are compared with theirs, and the differences are read one by one.

Go live with exception routing

Clean documents flow through. Exceptions queue for a person with the evidence attached, and the exception rate is reported every week.

In production at Strataigize

We run this ourselves.

Our own AI Operations Agent turns recorded meetings into written digests for the team, and our reporting pipeline reads raw exports from the advertising platforms into one client record each week under a rule that a client whose facts came back thinner than last time is not published. The exception-first design on this page came from that work.

Chuck, our AI Operations Agent

Governance

Controls and human review.

A failed validation is always an exception for a person, never a best guess written to your system
Every extracted field keeps its source page and its confidence, so an auditor can trace a record to a document
Documents are processed inside your tenancy or in a region you choose, and never used to train a shared model

The rest of the posture is published on trust and security: client-granted access you can revoke, credentials in managed stores, an AI governance summary aligned to the NIST AI Risk Management Framework, our subprocessors, and a DPA on request. We do not hold SOC 2 and we say so there.

Price

Prices in the open.

Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000 per month. Every AI build starts with a $7,500 AI Opportunity Audit over two weeks that is credited toward the build if you proceed, and a pilot is one bounded workflow on your real data over 30 to 90 days with a written success metric and a kill threshold.

The full price list is on the pricing page. For the factors that change a quote, read how much AI agents cost in 2026.

Questions

Document processing agents, answered.

What is AI document processing?

Reading documents that were made for people, such as invoices, contracts, applications and forms, and turning them into structured records a system can act on, with checks that catch what the model misread. The older name for the narrow version is intelligent document processing, or IDP.

How accurate is AI document extraction?

It depends on the document, and it has to be measured rather than quoted. We hand-label a sample of your real documents first, report field-level accuracy against it before the build proceeds, and keep that set as the regression test for every later change. A vendor quoting one accuracy figure for all documents is quoting a marketing number.

Can it handle contracts and leases as well as invoices?

Yes. Contract review, lease abstraction and application processing are the same pipeline with a different field list and different checks: clauses instead of totals, dates and parties instead of vendor numbers. The evaluation set is built the same way.