AI proposes / a person approves
Map unfamiliar shipment files to EDI without trusting an AI guess
Extract structured shipment rows from a packing list or document, inspect confidence and critical fields, then activate an exact-layout recipe only after confirmation.
Direct answer
Direct answer
AI-assisted EDI mapping is useful for discovering purchase order, shipment, item, quantity, unit, container and date values in PDFs or images. It should not send guessed business data directly to a retailer. Shipment Sentry constrains extraction to the canonical shipment schema, strips unsupported fields, requires human confirmation of critical values, and fingerprints the approved field layout before any later automation.
Why mapping needs a controlled workflow
Visual documents are variable and probabilistic extraction can confuse characters, rows and totals. The safe boundary is a proposal: AI lowers the first-mapping effort, while deterministic schema checks, human approval, exact-layout matching and output validation own the production decision.
What the control should check
- 01Accept only PDF, JPEG, PNG and WebP bytes that match their real MIME type.
- 02Constrain AI output to 1–1,000 rows and an explicit shipment-field allowlist.
- 03Expose per-field confidence and mark low-confidence evidence for review.
- 04Require purchase order, shipment, item, quantity and container confirmation.
- 05Expire mapping drafts and cryptographically bind them to workspace and route.
- 06Re-run extraction but reuse conversion only against the approved heading signature.
Working example
PDF: PO-77821 / ASN-1042 / SKU-104 / 12 EA / SSCC 000123456789012343
AI PROPOSAL → HUMAN CONFIRMATION → CANONICAL SHIPMENT → VALIDATED PARTNER OUTPUTAI provides evidence extraction, not autonomous retailer compliance. Missing required output data or a changed layout stops conversion.
Failure modes worth catching
- OCR turns an SSCC zero into the letter O.
- A page subtotal becomes a shipment line.
- Prompt content in the uploaded document attempts to add unsupported fields.
- A new packing-list layout inherits a prior approval.
Put it into the dispatch workflow
- 1Create the destination partner route.
- 2Upload one representative PDF or image.
- 3Review the extracted rows, confidence and critical field proposal.
- 4Confirm and name the mapping recipe.
- 5Resubmit matching documents through SFTP, email or API and inspect every output decision.
Questions
Implementation answers
Does AI automatically send an ASN?
No. The first layout always requires confirmation, and Shipment Sentry produces a validated output for the configured route rather than silently submitting guessed content.
Are document instructions allowed to change the schema?
No. The extraction client uses a fixed field allowlist and ignores unsupported keys returned by the model.
Can the same recipe work for CSV later?
No. Recipes are bound to their normalized source format and exact heading signature; a different source format receives its own confirmation.
Sources and limits
Reviewed 2026-07-19. Public references cannot establish every partner-specific rule. Current implementation guides and agreements remain controlling.
Keep investigating