The most expensive keyboard in your shop is the one retyping customer POs. Here's what actually happens in the four seconds between dropping a PDF into Workover ERP and a sales order appearing.

STEP ONE: READING THE DOCUMENT

OCR extracts the raw text and layout — PO number, ship-by date, terms, and the line-item table with descriptions, quantities, and unit pricing. Layout awareness matters more than raw character accuracy: a faxed PO with a skewed table is normal input in the oilfield, not an edge case.

STEP TWO: MAPPING TO YOUR WORLD

The customer writes "7-1/16 10K FLG RE-MFG"; your part master says something different. Machine learning bridges that gap by matching descriptions against your part numbers, price books, and this customer's order history. Every match carries a confidence score, and the model learns from each correction your team makes — so the customers you serve most become the customers it reads best.

STEP THREE: HUMANS WHERE IT COUNTS

High-confidence extractions become a sales order in one click. Low-confidence fields are flagged for a person to confirm — a highlighted "is this qty 12 or 72?" beats silent wrongness every time. The goal isn't zero humans; it's zero retyping.

STEP FOUR: ONE JOB PER LINE, TRACKED FROM THE START

Each SO line generates its own work order carrying the estimate as its cost baseline, so estimate-vs-actual tracking starts before the first chip is cut and rolls up to a live sales-order margin. The same engine also reads vendor invoices for the AP three-way match — the paperwork works both directions.

See it on your data. Bring one real job — a quote, a traveler, and the invoice — and watch it flow end to end in a 30-minute live demo.