// Industry
Logistics AI development
AI for logistics and supply chain — forecasting, automation, and optimization grounded in your operational data.
// The problem
Why this is hard
Logistics AI lives or dies on messy, real-world data and systems that already exist. A model that forecasts beautifully on clean history but ignores a depot closure, or can't talk to your TMS/WMS, doesn't move a single pallet. The engineering that matters is grounding on your operational data, integrating with what you run, and staying reliable when the day goes sideways.
// What matters here
The capabilities that move the needle
AI Agents & Automation
Automation for exceptions and back-office workflows, with guardrails so a bad step is caught, not shipped down the line.
RAG & Knowledge Systems
Answers grounded in your SOPs, contracts, and ops data, with citations — so ops teams get real facts, fast.
MLOps & Infra
Durable pipelines, observability, and cost control for models that run continuously against live operations.
LLM Integration & Evals
Document and comms processing (BOLs, customs, emails) behind an eval suite, integrated with your existing systems.
// Proof
Shipped in production
Logistics — Demand forecasting
stockouts
over the first two quarters
forecast accuracy
vs. the prior baseline
// FAQ
Common questions
// Related
Manufacturing
AI for manufacturing — quality, forecasting, and process automation built on your operational and sensor data.
E-commerce
AI for e-commerce — support automation, search, and personalization that convert without going off the rails.
// Let's build
Building AI for logistics?
Tell us where you are. We reply within a day with a concrete next step.