Document AI that reads the paperwork so your team does not.

On typical purchase invoices, well-implemented extraction reaches high accuracy on header fields and somewhat lower on line items, which is why confidence scoring and a review queue matter more than the headline figure. We refuse to quote a number before the pilot: we run several hundred of your own documents, score each field against the correct answer, and give you the measured rate for your supplier mix.

Materially, yes. Older OCR relied on templates mapped to each supplier’s layout and broke whenever one changed, which is why most implementations quietly died. Layout-aware models read documents they have never seen by understanding structure and context, so the long tail of suppliers becomes viable rather than being the part you still do by hand.

For the ones the system is not confident about, and that is by design. Every field gets a confidence score, anything below your threshold goes to a review queue, and nothing posts unless it also reconciles against your ledger and orders. You set how cautious it is, and we start cautious and relax it on evidence.

Purchase and sales invoices, purchase orders, delivery notes and PODs, certificates of conformity, test reports, timesheets, remittance advices and application forms. The practical requirement is a reasonably consistent set of fields and a way to validate the extraction against data you already hold.

Only if you agree to it, and the pipeline is designed around whatever constraint you set. Documents can be processed in your own cloud tenancy in a chosen region, with contractual terms preventing the use of your data for model training. For genuinely sensitive material we can keep processing entirely within your environment.

There is a build cost for the pipeline, validation and review interface, then a small per-document processing cost. For most mid-sized finance teams the running cost is a fraction of the labour it replaces, and the build is usually justified by early-payment discounts and faster month end alone.

We will not quote one, and we would be wary of anyone who does. The pilot runs several hundred of your own documents and scores each field, which gives you a real number for your supplier mix. Header fields typically do better than line items, which is exactly why confidence scoring and a review queue matter more than the headline figure.

It should not notice. Layout-aware models read documents by structure and context rather than by a mapped template, which is the main thing that has changed since template-based OCR. A supplier redesigning their invoice was what used to break these systems and is the single biggest reason older implementations were abandoned.

Yes, and it is usually where most of the value is. Three-way matching between purchase order, delivery note and invoice is rules work once the data is extracted reliably, and it is the part currently done by eye. Only genuine discrepancies then reach a person, which is the work that actually needs judgement.

There is a build cost for the pipeline, validation and review interface, then a small per-document processing cost — typically pennies rather than pounds at any reasonable volume. For most finance teams the running cost is a fraction of the labour it replaces, and early-payment discounts alone often cover the build.