Logistics use case
Document-to-cash for Durban logistics operations.
Every shipment produces a paper trail: manifest, bill of lading, customs file, proof of delivery, invoice. The money only moves once those documents agree. This page describes the workflow we build to make them agree faster, with people approving what matters.
The operational problem
Documents arrive by email and portal in inconsistent formats. Staff re-key reference numbers, cross-check line items between systems, and chase discrepancies over phone and email. Errors surface late, at invoicing or at the gate, when they are most expensive to fix.
The current workflow, typically
Inbox triage, manual capture into the TMS or ERP, spreadsheet reconciliation, ad-hoc queries to shipping lines and clearing agents, and a month-end scramble to match invoices to proofs of delivery before billing runs.
The proposed system
S1
Extract approved fields from manifests, bills of lading, customs files, PODs and invoices, whatever the format.
S2
Compare records across documents and the system of record; agreement passes straight through.
S3
Build an exception queue where documents disagree, each item carrying its source evidence and a proposed resolution.
S4
Post approved outcomes to the TMS, ERP or billing system and log every action for audit.
Required systems and data
- Document sources: shared inbox, portals, EDI or SFTP drops
- System of record: TMS, ERP or billing platform with API or file access
- A few hundred representative historical documents for evaluation
- The people who resolve discrepancies today
Human approval points
- Every exception is resolved by an authorised person
- Postings above agreed value thresholds require sign-off
- Customs-facing submissions are always human-approved
Risk controls
- Field-level confidence thresholds route uncertainty to people
- No unattended writes to the system of record at launch
- Full audit log of extractions, comparisons and approvals
- POPIA-aligned handling of any personal information in documents
Evaluation and measures, agreed before the build
A golden evaluation set is built from your own historical documents, with acceptance thresholds agreed per field. Baselines are captured first so the change is measurable. We do not publish generic accuracy or saving percentages; targets are set per engagement.
- Reconciliation cycle time
- Exception rate and exception age
- Missed or late document rate
- Rework and credit-note volume
- Days from POD to invoice
Implementation stages
Delivery follows the Forward-Deployed AI Sprint: a one-to-two-week diagnostic to map the document flow and baseline it, a four-to-six-week build of the smallest valuable slice, then run-and-improve with monitoring and regression checks.
Feasibility constraints
- Depends on lawful access to the systems and documents involved
- AgentAligned has no integration with any Port authority system; congestion or vessel data enters only where the client lawfully has it
- Very low document volumes may not justify the build; the diagnostic answers this honestly
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