Logistics software case studies
Real logistics and supply chain engagements: document AI for BOLs and customs paperwork, freight invoice audit, TMS modernization, and exception triage — with scope, timeline, and measured outcomes.
The logistics work that pays back fastest is document-heavy and measurable: BOL and customs extraction, freight invoice audit, and exception triage. Across our supply-chain engagements, first production features shipped in 10–16 weeks, extraction landed at 95–98% field-level accuracy against real carrier document mixes, and audit recovery ran 1.5–3x manual review. Request a quote and we'll scope the same path against your own document sample.
Engagement snapshots
| Engagement | Scope | Timeline | Outcome |
|---|---|---|---|
| Freight document intake | BOL, POD, rate confirmation, and customs extraction with low-confidence routing to a human queue | 12 weeks to production | 96.8% field-level accuracy; coordinator handling time down ~45% |
| Freight invoice audit | Accessorial, duplicate-billing, and contract-rate mismatch detection wired into the payables workflow | 10 weeks | 2.1x recovery vs the prior manual audit sample |
| Exception triage | Dollar-impact ranking of shipment exceptions with suggested resolution paths for coordinators | 14 weeks | Exceptions resolved same-day rose from 52% to 81% |
| TMS modernization | Strangler-pattern migration of a 14-year-old .NET TMS module set to a modern API surface | 9 months, phased | Zero-downtime cutover; release cadence from quarterly to biweekly |
What these engagements had in common
- Real document distribution, not a curated sample — Accuracy baselines were measured against the live carrier and broker mix in week one. This is the single largest reason logistics AI pilots overstate results.
- A dollar value per case — Every use case had a measurable per-transaction value, so it defended itself in the next budget cycle.
- TMS data access confirmed before scoping — Historical shipment data access is usually the longest pole in the timeline.
- A human already in the loop — Low-confidence output routes to the coordinator who was doing the work anyway — adoption is immediate and no one is asked to trust a black box.
How we scope and quote
A 4-week prototype sprint against your real document mix ($40K–$90K) sets an honest accuracy baseline. Production builds run $80K–$400K per feature with $8K–$30K/month to operate. Modernization work is scoped after a two-week architecture assessment. Delivery is US-based W-2 senior engineers working inside your tenant and repositories, under your MSA. Send us a document sample or a system diagram and we'll return a scoped quote with the accuracy targets we're willing to commit to.
Common questions
What logistics software projects does NextGen take on?
Document AI for bills of lading, proofs of delivery, rate confirmations, and customs paperwork; freight invoice audit; exception triage; lane-level ETA prediction; and modernization of legacy TMS, WMS, and EDI integration layers.
How accurate is document extraction on freight paperwork?
Production systems land at 95-98% field-level accuracy with low-confidence fields routed to a human queue, provided accuracy is measured against your real carrier document mix rather than a curated sample.
How long before a logistics AI feature is in production?
A 4-week prototype sprint establishes the baseline, and first production features typically ship 10-16 weeks from kickoff when TMS data access is confirmed early.
What does a logistics engagement cost?
Prototype sprints run $40K-$90K, production features $80K-$400K, and ongoing operation $8K-$30K per month. Modernization programs are quoted after a two-week architecture assessment.
How do I request a quote?
Send a representative document sample or a system diagram through the contact form. We run a free 30-minute scoping call with a senior engineer and return a written scope with accuracy and timeline targets.
Have a specific situation? Talk to an engineer at NextGen — we do free 30-minute scoping calls with a senior developer, not a salesperson.
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