Can it handle documents from any carrier?

Layouts vary but the fields are conventional, so extraction generalises well across carriers. New formats improve with a small number of samples rather than requiring a bespoke template each time.

What about rate discrepancies on carrier invoices?

Extracted charges are compared against your agreed rate card and anything outside tolerance is flagged with both figures shown. Recovering incorrect charges is frequently where this pays for itself first.

What accuracy should we expect?

Clean native PDFs of a consistent format extract very reliably. Poor scans and highly variable layouts are harder. We benchmark on your actual documents before committing to a threshold, and the confidence scoring means low-certainty extractions get reviewed rather than silently posted.

Do we still need someone reviewing documents?

For a much smaller share. The goal is that your team reviews the exceptions rather than every document — which typically means a fraction of the volume, handled in seconds each rather than minutes.

Document AI · Logistics

Document AI for Logistics

Every consignment carries paperwork somebody has to read.

Bills of lading, commercial invoices, packing lists, customs declarations, and proof of delivery all arrive as documents and all end up as data in your TMS via a keyboard. Document AI reads them on arrival, validates against the booking, and posts what matches.

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Per document: SecondsPer document
Field confidence: ScoredField confidence
Full trail: AuditedFull trail

Typical stack

Azure Document AI
OpenAI Vision
Python
Postgres
Queue Workers

Use cases

What this handles for logistics operators

Extract bill of lading and commercial invoice data straight into the TMS
Validate customs documentation for completeness before submission
Match proof of delivery against consignments and flag discrepancies
Process carrier invoices and check them against agreed rates automatically

Shipping paperwork processed on arrival, with discrepancies flagged before they become disputes.

Context

The logistics problem underneath it

Logistics runs on documents and status enquiries — bills of lading, customs paperwork, proof of delivery, and an endless stream of 'where is my shipment' calls. Both are highly automatable, and both currently consume operational staff who should be handling actual exceptions.

Shipping documents arrive in every format and get manually keyed into the TMS
Status enquiries flood phone and email, most of them answerable from tracking data
Exceptions and delays are spotted late because nobody is watching continuously
Carrier rates and capacity live across portals, spreadsheets, and inboxes

What we build

Inside a document AI pipeline

We design for the ninety percent and route the rest to people. Documents that extract cleanly and pass validation flow straight through. Anything below your confidence threshold, or failing a business rule, lands in a review queue where a person fixes it in seconds — and that correction feeds back into extraction quality.

OCR and layout parsing for scans, photos, native PDFs, and email attachments
Field extraction with per-field confidence scoring rather than all-or-nothing output
Validation rules that check totals, dates, references, and cross-document consistency
Human review queues for low-confidence extractions with single-screen correction
Straight-through posting into ERP, accounting, or CRM systems
Classification that routes each document type to the right pipeline automatically

Deliverables

What ships with the engagement

Extraction schema per document type agreed with your team
Accuracy benchmark measured on a sample of your real documents
Review queue interface for exceptions
Integration writing extracted data into your system of record
Retention and audit configuration matching your compliance requirements

FAQ