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Iotron AI

Project scenario · Logistics & Supply Chain

How we would replace manual re-keying of shipping documents with extraction, validation and a review queue, measured field by field.

Typical starting point: freight forwarder and logistics operator. This scenario shows how we approach this class of problem. It does not describe a named client engagement, so no results are claimed.

Typical client
Freight forwarder and logistics operator
Industry
Logistics & Supply Chain
Built with
Iotron DocuMind · OCR · Layout-aware extraction models · LLM-assisted field normalisation
Measured against
4 agreed measures

01The challenge

Back-office teams re-keying data from thousands of documents in dozens of layouts, with errors surfacing downstream in billing and customs.

Where we usually start

Documents arrive by email and portal as PDFs, scans and phone photos, from hundreds of carriers and suppliers. Layouts change without notice, some fields matter far more than others, and the ERP and transport management system expect clean, validated values.

02What it is measured against

How we would know it worked.

Agreed with the business before any model is built, and reported against throughout — so success is defined by the operation, not by the model.

Measures this project is reported against
#Measure
01Per-field accuracy on a held-out test set, for the fields that matter
02Share of documents posted without human touch
03Operator time per document
04Downstream billing and customs corrections

03The Iotron solution

What we build.

Iotron DocuMind: classification and field extraction across document types, validation against purchase orders and shipment records, confidence scores per field, and a review queue where operators correct only what the system is unsure about — every correction feeding back into the models.

How the project runs

  1. Weeks 1–2

    Document inventory: types, volumes, layouts and the fields that drive billing and compliance; build a labelled test set.

  2. Weeks 3–6

    Extraction and classification pipeline, validation rules against ERP and TMS data, and per-field accuracy reporting on the test set.

  3. Weeks 7–9

    Review queue in production for one document type; operators correct low-confidence fields and the thresholds are set together.

  4. Weeks 10–12

    Straight-through posting for high-confidence documents, then the next document types in order of volume.

Technology

  • Iotron DocuMind
  • OCR
  • Layout-aware extraction models
  • LLM-assisted field normalisation
  • Validation rules engine
  • Review queue
  • ERP / TMS integration

04Lessons from this kind of work

What we would tell the next team.

  • One overall accuracy score hides the field that breaks billing; measure each field separately.
  • Confidence thresholds are a business decision, set with the people who own the errors.
  • The review queue is where the system keeps improving — design it as carefully as the model.
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