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Logistics

Fleet-wide route optimization at national scale

A produce-handling rule decided which shipments could share a truck. Encoding that rule into the routing algorithm itself — not checking it by hand afterward — is what made the system usable at fleet scale.

✕ PRODUCE WINDOW DEPOT A DEPOT B DEPOT-AWARE ROUTING · HARD CONSTRAINTS ENCODED
Case study

How it broke down

Problem

A Fortune 100 grocery retailer needed its entire delivery fleet routed against supply from multiple depots — at a scale generic routing tools weren't built to handle.

Constraint

Produce doesn't behave like the rest of the inventory. Shipments that looked combinable on paper couldn't share a route once produce-handling rules applied, and that rule had to be a hard constraint the optimizer respected — not a manual check layered on afterward.

Approach

Supply depots were folded directly into the routing model, so the optimizer weighed depot assignment and route combination together, with the produce-handling rule encoded as a hard constraint from the start — the same constraint-first principle behind every engagement.

Outcome

The same depot-aware, constraint-first pattern was delivered again for a nationally distributed beverage brand, without re-deriving the approach from scratch.

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Founded by scientists turned AI leaders — national-lab research backgrounds, then senior data science and AI roles shipping production systems across logistics, security, and adtech.