AI Document and Invoice Processing Automation

Invoices are the classic case — the prebuilt invoice model reads vendor details, dates, totals, taxes, and line items without any training. Purchase orders, receipts, delivery notes, and your own recurring forms are also common; anything with a consistent-enough structure can be handled with prebuilt or custom-trained models, which we confirm against your real samples during scoping.

Documents arrive in a shared mailbox, SharePoint library, or upload point. The extraction model reads the fields and returns them with confidence scores. Power Automate validates the data against your business rules, routes low-confidence or failed items to a human review queue, sends clean items through your approval chain, and posts approved data to your finance system. Every step is logged.

We deliberately don't quote an accuracy percentage before seeing your documents — accuracy depends on document quality and variety, and a number invented in a sales page would be worthless. During the pilot we measure extraction on your real documents and set review thresholds from those measurements, so what goes to auto-approval is based on evidence you have seen.

Only the exceptions. Confidence thresholds route uncertain extractions, unknown vendors, and failed validations to a review queue; clean documents flow through untouched. That human-in-the-loop design is intentional — it is what makes the workflow defensible to your auditors.

Any system reachable through Power Platform connectors or an API — Dynamics 365 and Dataverse natively, and many accounting platforms via standard, premium, or third-party connectors. Where no direct path exists, we agree an intermediate such as a Dataverse table, SharePoint list, or structured file your system imports. The destination and its constraints are confirmed at design, not discovered at cutover.

Whichever fits your volumes, document variety, and licensing position — that's a design decision we make with you, with the cost implications on the table. AI Builder lives inside Power Platform and is capacity-licensed; Document Intelligence is an Azure service billed on consumption with strong prebuilt models. Both plug into the same Power Automate workflow, so the choice doesn't change the process your team experiences.

Running costs are Microsoft licensing and consumption: Power Automate premium licensing where premium connectors are used, plus either Document Intelligence consumption or AI Builder/Copilot Credits capacity. The pilot produces a real per-document cost estimate for your volumes — we won't hand you a guess.

Microsoft is transitioning AI Builder licensing to Copilot Credits: new capacity has been sold as Copilot Credits since November 2025, and Microsoft has announced that AI Builder credits seeded in existing licenses are removed on November 1, 2026. We validate your licensing position at design and build on the model that will still be licensable for you next year.

A scoped build: discovery, design, extraction configuration, the Power Automate workflow with review queue and approvals, integration to one destination system, pilot, cutover, runbook, and handoff. The quote is fixed-price in writing before work begins, and you pay after you approve delivery. More document types, multiple destinations, or complex custom models raise the quote — in writing, before work starts.

A typical single-workflow build — one or two document types, one destination system — takes about four weeks including the pilot. Custom model training for unusual layouts, multiple destinations, or slow access provisioning extends that, and the written quote states the timeline for your actual scope.

Representative document samples (including the messy ones), your validation and approval rules, access to the destination system, a named process owner, and the Power Platform environment access to build in. Weak samples are the most common cause of pilot surprises, so we push on that early.

No. We build the document pipeline that feeds the system you already run — we don't implement, migrate, or reconfigure the ERP itself. If your destination system genuinely can't receive the data, we'll say so at design and agree a workable intermediate rather than scope-creep into an ERP project.

Per Microsoft's documented data-privacy commitments for its Azure AI services and Power Platform, your documents and extracted data are not used to train Microsoft's foundation models. Custom models trained on your samples exist in your environment for your use. Your compliance team should review Microsoft's current terms against your specific requirements.

The prebuilt invoice model supports a wide range of locales, currencies, and languages, and multi-entity routing is a workflow-design question we handle in the approval matrix. Each adds scope, so entities, currencies, and languages in play are declared at design and covered in the written quote.

Your team, using the runbook — the workflow runs on your tenant and your licenses, with no dependency on us. If you'd rather not own the monitoring, ongoing support can be contracted separately; the honest default is that a well-built flow with a staffed review queue needs very little care.