AI development for logistics and supply chain

Highest-ROI logistics AI use cases

• Document automation (BOL, POD, rate cons, customs) — Semi-structured, high-volume, and currently handled by people retyping PDFs. Typical target: 95-98% field-level accuracy with low-confidence routing.
• Freight invoice audit — Catching accessorial errors, duplicate billing, and contract-rate mismatches. Value is directly measurable in recovered dollars.
• Exception management and triage — Ranking shipment exceptions by dollar impact and suggesting the resolution path, with the coordinator deciding.
• ETA prediction on your own data — Carrier-published ETAs are often worse than a model trained on your historical lane performance.
• Email and EDI intake normalization — Turning unstructured broker and shipper email into structured orders — a workflow still running on human copy-paste at most operators.

Why logistics AI projects stall

• Starting with route optimization — It's the most requested and the hardest to prove: the counterfactual is unobservable, so nobody agrees the model helped.
• Underestimating document variety — Every carrier, broker, and customs regime has its own layout. Accuracy on a 50-document sample means little; measure against production distribution.
• Data locked in the TMS — Access to historical shipment data is often the longest pole in the timeline. Confirm it in week one, before scoping.
• No dollar value per case — Use cases without a measurable per-transaction value can't defend themselves in the next budget cycle.

Realistic targets

How we engage

A 4-week prototype sprint against your real document mix ($40K–$90K) establishes an accuracy baseline on production distribution, not on a curated sample — which is the single most common reason logistics AI pilots overstate their results. From there, production builds typically run $80K–$400K per feature with $8K–$30K/month to operate. US-based W-2 senior engineers, working inside your tenant and repositories, under your MSA.