Logistics
AI for warehouses, fleets, and freight — built for the messy reality of physical operations.
Logistics operations are constraint-rich and data-rich. The wins come from systems that respect operational constraints (capacity, regulations, driver hours, weather) — not from clever models in isolation.
Where AI fits
High-leverage use cases in this industry.
01
Warehouse automation & optimization
Pick path optimization, slot planning, robot orchestration, exception handling. Tightly integrated with WMS and order management.
02
Route optimization with real constraints
Routing that respects driver hours, vehicle capabilities, delivery windows, weather, and live traffic. Re-optimized in operations, not just at planning time.
03
Document & customs automation
Extraction from bills of lading, customs forms, freight invoices, and proof of delivery. Audit-ready, with human review at the right thresholds.
04
Demand forecasting & capacity planning
Network-level forecasting tied to your capacity planning and procurement cycles. Built for your fleet, your warehouses, your seasonal patterns.
How we approach this industry
A pattern that respects logistics realities.
- Assess01
Industry-aware discovery
Two-week sprint scoped to your operational reality — regulatory, OT/IT, integration constraints baked in from day one, not discovered mid-build.
- Design02
Reference architecture
Topology that fits your facility, your cloud, your compliance. On-prem, hybrid, or cloud — the call is documented before we start building.
- Build03
Pilot on one site / line / product
Working system on one part of your operation. Measurement window with explicit success criteria. Go/no-go decision before scaling.
- Operate04
Roll out & operate
Multi-site rollout if pilot succeeds. Central observability, drift detection, runbook. Optional retainer for ongoing operations.
Frequently asked
Industry-specific questions.
We already have a TMS / WMS — does AI work alongside it or replace it?
Alongside. Our systems plug in as a planning or execution layer on top of your existing TMS/WMS — the system of record stays where it is. Easier to roll out, easier to roll back if something breaks.
How do you handle the inevitable exceptions in physical operations?
Exceptions are first-class. Every system we ship has a clear path: AI handles the 80% routine, escalates the 20% to a human with full context, and learns from the human resolution. Exception handling is part of the eval suite, not an afterthought.
Can the system work during ERP downtime / network outages?
Depends on the use case. For warehouse-floor systems, yes — edge deployment with graceful degradation. For cloud-integrated planning, no — but recovery and reconciliation are designed in. We scope this explicitly during discovery.
How do we compare against the existing planning system?
Shadow mode first — AI runs in parallel, generates recommendations, but operations follows the existing system. Compare for 2–4 weeks. Then gradual cutover with A/B by route, region, or warehouse. No big-bang switchovers.