Document Intake & Data Entry Automation
Vehicle Services · 2026 · Back-Office Automation
Around 100 tickets a day are read, matched and filled in automatically — roughly 3,000 fields nobody types. About 40% open with no human touch; the rest arrive pre-filled for a person to confirm.
The challenge
A vehicle services company receives repair quotes from thousands of partner workshops. They arrive in every format a garage can produce: a PDF from a management system, a photo of a printed sheet, a page written by hand. They land across several mailboxes, mixed in with all the other post.
Someone opened each one and retyped it. Vehicle, parts, quantities, prices, the workshop itself — around 30 fields per quote, roughly 100 quotes a day. That is about three thousand fields typed into the management system every working day, and nobody reads the thirtieth field of the hundredth quote as carefully as the first.
The request was not "classify our documents". It was: stop the typing.
What we built
A document intake pipeline that runs in the client's own cloud account, from mailbox to open ticket:
- An ingester reads the mailboxes and separates quotes from everything else that arrives there.
- An extraction agent reads each quote — PDF, photo or handwriting — into structured fields.
- A matching layer resolves what the workshop wrote into what the management system actually holds: the part in the client's own catalogue, the workshop in their own records, the value each dropdown will accept. Nothing lands until it clears that layer, which is a build of its own and a story of its own.
- A review dashboard shows the operator a quote already filled in, marks what is missing or uncertain, and opens the ticket.
The threshold between "open it" and "show it to a person" is a setting, and we tuned it to be suspicious. A wrong ticket costs far more than a slow one, so at the first sign of doubt the quote goes to a human. About 40% of quotes clear that bar and become tickets with nobody touching them. The rest do not, on purpose.
What the person receives is not a blank form. It is the quote, read, matched and pre-filled, with the uncertain fields flagged. The human confirms; the human does not type. And the corrections made in review are not thrown away: they are read back on the next quote, which is the second half of this build.
The results
Around 100 tickets a day come out of the pipeline. That is roughly 3,000 fields read and matched with nobody typing them — the work that used to fill the day.
About 40% of quotes are ticketed untouched. The rest is not work left over: it is work that arrives already read and already matched, waiting for a yes.
| Number | What it measures |
|---|---|
| ~3,000 | fields read and matched per day |
| ~100 | tickets a day out of the pipeline |
| ~40% | of quotes ticketed with no human touch |
| ~30 | fields per quote, read and matched |
What you get
The intake pipeline running in your own cloud account — mailboxes in, structured records out — plus the review dashboard your team confirms from, and the confidence threshold set where you want it. Process audit first, scope in writing, and 30 days of support. It is packaged as Back-Office Automation.
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