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A parcel sorting conveyor in a dark warehouse, boxes moving along the rollers under strip lights.

Service 03 · AI Process Automation

Hand the routine work to a model

Your team spends hours reading, sorting and routing items a model can handle. So we automate that part and keep a person on the cases it is unsure of.

Book a consultationSee the case
  • Documents, images and text read into data
  • Each item classified and routed
  • Unsure items sent to a person
  • Accuracy checked on your items first

Example record

Each item, and where it went

So you can audit a decision after the fact. Each item is logged with its confidence and its route.

Your team sets the thresholds and owns what goes to review.

queue · incoming promotionsSample log

  • Advert cropped off the page

    confidence above threshold

    Done

  • Offer type read as multi-buy

    matched to the product record

    Routed

  • Handwritten price unclear

    below threshold, sent to a person

    Review

  • Page in an unknown layout

    no match, held for the team

    Held

Where automation projects stall

A model that works on a sample often stalls on the full volume. So these get checked before rollout.

  • A process automated before anyone measured it

  • Accuracy reported on clean samples

  • No route for the items the model is unsure of

  • Output that never reaches the system that acts on it

Capabilities

What goes into your build

So one team carries the work, process mapping through running in production.

01Process mappingThe steps, the volumes and the decisions, measured before anything is automated.
02Document and image readingOCR and vision models that read printed and handwritten text into structured fields.
03Text classificationLanguage models that sort messages, reviews and requests by type and urgency.
04Human review queueItems below the confidence threshold sent to a person, with the reason attached.
05System integrationResults written into your ERP, CRM or ticketing system.
06MonitoringAccuracy and volume tracked after launch, so drift shows early.

What it has done for clients

Our published work, with the figures as the client cases state them.

  • Printed promotions cropped with 99.3% accuracy

  • Offer type recognised with almost 93% accuracy

  • Unwanted reviews predicted with 80% accuracy

  • Routine issue requests cut 10x on a 7,000-channel network

Model

What we fix, and what you decide

What we fix

  1. F1The process measured before it is automated
  2. F2Accuracy reported on your own items
  3. F3A review route for low-confidence items

What you decide

  1. C1Which process goes first
  2. C2The confidence threshold
  3. C3Which systems receive the results

Related

Where to go next

Next step

Send us a week of your queue.

You learn which part a model can take and what stays with your team. That gives you a go or no-go before you fund a build.

Book a consultation →