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Nodeyra.

Illustrative project

LogisticsAI automationDocument processing

AI-assisted document processing for logistics teams.

A proposed workflow for reading supplier documents, checking extracted information, and routing exceptions to the operations team for review.

Concept preview, sample data: a document review screen with a supplier invoice beside its extracted fields, one value flagged for review, and approve and correct controls
INDUSTRY
Logistics
PROPOSED SCOPE
Document extraction, review workflow, and system integration
TYPICAL TECHNOLOGIES
Python · TypeScript · PostgreSQL · React

The challenge

Logistics operations run on documents: delivery notes, supplier invoices, customs paperwork and proof of delivery. Someone has to read each one, type the important details into the business system, and compare them with what was ordered. The work is steady and repetitive, and it competes with the parts of the job that need judgement.

Accuracy matters because each value feeds a financial record. A wrong quantity or price leads to a supplier dispute, a payment correction or a stock count that no longer matches the shelf. The aim is not to remove people from the process. It is to take the typing and cross-checking off their desks while keeping them in charge of anything uncertain.

How the workflow would work

  1. Read and organise incoming documents

    Documents arrive by email, upload or scan and are sorted by type and supplier. An AI model then extracts the information into a consistent set of fields, such as supplier, reference, line items, quantities and totals, so every document is described the same way regardless of its layout.

  2. Check information before updating records

    Each extracted value is compared with existing records: the purchase order, the agreed price list and the expected delivery. Values that match move on. Values that are missing, inconsistent or that the model is unsure about are flagged individually for a person to check, rather than the whole document being trusted or rejected.

  3. Make exceptions easy to review

    Flagged items appear in a short review queue showing the document, the extracted value and what it was compared against, side by side. A reviewer confirms or corrects the value in one step. Nothing is written to the business system until the checks pass or a person has approved it.

  4. Help the team maintain the workflow

    Progress is saved at every step, so processing resumes after an interruption or while waiting for an approval. Updates are sent to the business system at a pace it can handle, and the workflow checks for an existing record before retrying an update, so a retry never creates a duplicate. Corrections made by reviewers are kept as test cases, so changes to the workflow can be checked against real examples.

Intended outcomes

What a successful engagement would deliver, and how each outcome would be measured.

  • Less time spent on manual entry

    Measured by timing the document-handling tasks for a representative week before the workflow is introduced and again once it is in regular use.

  • Fewer corrections after posting

    Tracked as the number of records that have to be amended after they reach the business system, compared over equivalent periods before and after.

  • A review queue the team can clear

    Assessed by the share of documents that need a person to look at them and the time each review takes, reported weekly so the thresholds can be adjusted.

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