Every detail in its place.
The operating challenge
A freight team receives invoices, packing lists, updates, and proof-of-delivery files across inboxes and shared folders. Operators retype references and chase missing documents. Inconsistent units or shipment identifiers can propagate through the process.
A readable extraction is not the same as a correct shipment record. The workflow must preserve source evidence, normalize units, compare documents, and make uncertainty visible to a responsible operator.
A workflow with a clear next step
01 / Receive and preserve
Scan files received through an approved channel. Store the original, its hash, and a durable source reference.
02 / Classify and extract
Identify document type and extract shipment references, parties, quantities, weights, and dates.
03 / Normalize and validate
Use code for arithmetic, unit normalization, date interpretation, and required-field checks.
04 / Match the shipment
Compare against the TMS. Ambiguous matches or multi-shipment files enter review.
05 / Show the discrepancy
Present the source page, extracted value, conflicting evidence, and proposed follow-up to an operator.
06 / Publish approved changes
Write approved corrections, re-read the TMS record, and retain the review history.
Architecture & controls
Approved mailbox or storage → OCR → schema validation → TMS integration → durable review queue. Choose industry event standards where supported by the actual carrier and TMS.
- Material commercial and regulatory changes require an authorized operator.
- Original documents remain available to reviewers.
- Thresholds are set by field risk; model or OCR confidence is not treated as proof.
- Customs declarations and bills of lading remain outside autonomous execution.
When things do not go to plan
Illegible scans, contradictory references, unknown units, and missing documents stay visible in review. Hashes prevent double processing. Source timestamps and system arrival times are tracked separately. A TMS outage queues writes without losing the original evidence.
The automation boundary: This design assists shipment administration. It does not independently make customs, regulatory, or contractual determinations.
Evaluation, before expansion
Include scanned documents, rotated pages, mixed shipments, decimal separators, conflicting weights, duplicate files, and missing references. Compare field errors by document type and criticality. Begin in shadow mode before operator-assisted writes.
| Measure | What we measure | Proposed criterion |
|---|---|---|
| Extraction quality | Selected fields on representative documents | Target: 98% exact match before normalization review |
| Operator time | Eligible document packets | Target: 30% below baseline |
| Exception control | Material mismatches surfaced for review | Gate: all critical mismatch tests escalated |
Targets are agreed with the process owner after measuring representative current work. A pilot also records error rates, review effort, operating cost, and exceptions. Released staff capacity is not automatically a cash saving.
Rollout & handover
Move through offline evaluation, shadow mode, and operator-assisted work before considering a limited automatic path. Each phase has a named owner, stop conditions, and a manual fallback. The approved scope determines the delivery schedule.
What the engagement would deliver
- Document intake and source preservation
- Field extraction and cross-document validation
- TMS draft integration
- Operator review with page evidence
- Exception alerts and replay procedure
The final handover includes client-owned access, configuration or source, known limitations, evaluation cases, and recovery instructions. Third-party subscriptions and ongoing support are agreed separately.
Technical references
These sources inform design considerations. They do not validate a deployed Tern & Loom project or establish vendor partnership.
DCSA: Track & Trace standard ↗
AWS: document extraction best practices ↗
Research reviewed 20 October 2026.