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Daily build note · June 24, 2026

Freight Exception Triage Studio

A local seeded logistics operations demo that ingests synthetic freight exceptions, scores urgency and automation readiness, maps workflow ownership, estimates cost-at-risk, requires human...

AI Product Management For Logistics, Freight Brokerage, Carrier/Shipper Operations, Dispatch, Pricing, Tracking, Exception Management, And Transportation Workflow Automation Build note published Public demo coming soon

What shipped

Freight Exception Triage Studio is a runnable local Python CLI that creates a synthetic freight exception workspace, scores the queue, generates a triage pack, and validates the generated artifacts.

Implemented commands:

  • python -m freight_exception_triage_studio sample --path /tmp/freight-exception-triage-studio-demo
  • python -m freight_exception_triage_studio triage --path /tmp/freight-exception-triage-studio-demo
  • python -m freight_exception_triage_studio generate-pack --path /tmp/freight-exception-triage-studio-demo
  • python -m freight_exception_triage_studio validate-pack --path /tmp/freight-exception-triage-studio-demo

Generated artifacts include a triage board CSV, exception brief, workflow map, ROI model, human approval policy, pilot rollout plan, and consolidated JSON pack.

Architecture

  • Standard-library Python only, with argparse, dataclasses, json, csv, pathlib, and simple deterministic scoring rules.
  • Seeded synthetic data is the source of truth for the MVP.
  • Scoring separates priority, cost at risk, automation readiness, owner routing, evidence needs, and approval boundaries.
  • Validation is local and strict so the demo can be trusted without external services.
  • Human approval gates are modeled as first-class output fields and policy language, not as a footnote.

Trimmed scope

  • No web UI.
  • No database.
  • No auth or billing.
  • No live TMS, tracking, ELD, carrier, compliance, email, SMS, EDI, browser, or provider integration.
  • No model calls or external APIs.
  • No real freight records.

Limitations

  • Scores are deterministic pilot heuristics, not learned predictions.
  • ROI assumptions are simple category-level estimates for discovery conversations.
  • The input contract is JSON only.
  • Human approvals are represented as policy and validation state; there is no workflow engine or signature capture.

Suggested next steps

  • Add an importer for a sanitized CSV export from a TMS or exception queue.
  • Add configurable scoring weights for a specific brokerage, shipper, or carrier operations team.
  • Add approval audit fields if the pack becomes part of a real pilot.
  • Add side-by-side baseline versus pilot reporting after operators review real queue samples.