Logistics & Supply Chain AI solutions.

Logistics AI solutions for demand forecasting, route optimization and freight documents. They run on dispatch and tracking apps that keep working in a warehouse dead zone and sync when the signal returns.

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What makes logistics AI hard.

The logistics AI challenges we design for before any code is written, so the AI holds up with real users, real data and real auditors.

Messy documents

Bills of lading, invoices and customs forms arrive as scans and emails. Document AI extracts them, with human review on low-confidence fields.

Offline in the field

Drivers and pickers lose signal. Apps must queue, sync and resolve conflicts without losing a scan.

Integration debt

WMS, TMS, ERP and carrier APIs stitched together with retries and dead-letter handling, so models and dashboards see one clean record.

Logistics AI solutions we build

The AI use cases we build for logistics teams, and the software platforms they run on. Each starts from a measurable goal, passes an eval gate before launch and reaches users through a gradual rollout.

The model work comes from our LLM and AI agent development team, tested by QA and AI evaluation. For results in production, browse our AI case studies.

Compliance we plan for

SOC 2 Type II
GDPR

✓Demand forecasting and planning
✓Route and dispatch optimization
✓Freight document extraction and automation
✓Offline-first driver and warehouse apps
✓Real-time shipment tracking
✓Carrier, ERP and IoT telemetry integrations

Logistics AI development services.

The WAMO Labs services most logistics AI engagements draw on, from LLM integration to the platform around it.

AI integration

Our AI integration services put LLM applications, AI agents and RAG systems into the products and workflows you already run. Measured by evals, guarded in production, with a cost per request you can predict.

Mobile apps

Mobile app development for iOS and Android, with native polish and AI features users actually use: on-device ML, LLM assistants, voice and camera intelligence, all backed by solid APIs.

Cloud architecture

Cloud architecture and DevOps that keep AI products fast, secure and affordable. MLOps pipelines, GPU inference and LLMOps on AWS, GCP or Azure, with costs you can forecast.

Questions about logistics AI projects.

Timelines, cost drivers and compliance, answered plainly. More on how we build and evaluate AI in our AI engineering insights.

Which logistics AI use cases do you build?

Logistics AI projects we build include demand forecasting and planning; route and dispatch optimization; and freight document extraction and automation. Each one ships with a versioned eval set, guardrails and a gradual rollout, on the platform work listed above.

How long do logistics AI projects take?

Most engagements start with a four-week immersion. By week 3 a first slice is live behind a feature flag for a small share of users, evaluated and instrumented. By week 4 you have a quarter-by-quarter roadmap with named owners. Full timelines depend on scope, integrations and compliance review.

What drives the cost of logistics AI solutions?

Four things: the systems we integrate with (WMS, TMS, ERP and carrier APIs), the compliance scope (SOC 2 Type II and GDPR), how much evaluation and human review the use case needs, and inference volume, because cost per request compounds with usage. We size all four during the four-week immersion, so you know what it costs to keep going before you commit to a roadmap.

How do you handle compliance and data privacy in logistics AI?

We plan for SOC 2 Type II and GDPR from the first sprint and decide which data may ever reach a model provider before anything is built. Every AI feature ships with access controls, an audit trail and human review wherever a wrong answer carries real risk.

Building AI in Logistics & Supply Chain?

Tell us the AI use case or product you're working on. We respond within one business day, with a real human and a real opinion.

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