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Illustrative scenario

A haulier buried in paper defect checks

  • Transport and haulage
  • Document extraction

Overview

How we'd approach extracting data from daily walkaround checks and driver paperwork for a small transport and logistics operator.

The situation

The pain point The office re-keys paper checks into a spreadsheet, and defects sometimes get missed for days.

A haulage firm running 40 vehicles collects daily walkaround checks, defect sheets and delivery notes on paper. Finding the record for a specific vehicle on a specific date means a hunt through a folder.

Diagram: a timeline. A driver spots a defect in the morning and notes it on the paper check, the paper gets back to the office at the end of the day, and the office re-keys it into a spreadsheet later. The defect is finally spotted days later, and finding one vehicle on one date means a hunt through a folder.

Where AI could help

The realistic opportunity here is document extraction: reading the handwritten and printed paperwork and turning it into structured, searchable records, with defects flagged automatically. Current AI does this well, provided the inputs are consistent enough.

Diagram: a pile of paper checks, defect sheets and delivery notes feeds an AI that reads them into records, if the forms are consistent enough. Defects are flagged automatically, straight away, and any vehicle on any date can be found.

How we'd approach it

  1. Intake. We'd look at the actual forms and how they flow back to the office.
  2. Opportunity scoring. Extraction would be scored against simpler fixes, because sometimes a better form beats any AI.
  3. Proof of concept. We'd run a batch of the firm's real, anonymised check sheets through a working demo and show the extracted, searchable result, so they can see the accuracy before committing.
  4. Report. Where it's reliable, where a person still needs to check, and the running cost per document.
  5. Build or handover. We build it, or hand a clear brief to their own team or supplier. Or we tell them plainly that the paperwork is too inconsistent for this to pay back yet.
Diagram: five steps, from intake and scoring against simpler fixes to a proof of concept on real check sheets before any build, then a report on where a person still checks and the cost per document, and build or handover.

What success would look like

In the proof of concept we'd measure extraction accuracy on their real forms, the office time saved on re-keying, and how quickly a defect surfaces. Those numbers, measured on their data, are what the decision should rest on, not a figure from someone else's case study.

Diagram: three things measured today and in the proof of concept: extraction accuracy on real forms, office time spent re-keying, and how quickly a defect surfaces. The decision rests on the firm's own numbers.

See it on your own data

Every business is different, so the proof of concept is where we find out whether this works for yours.

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