[00] Introducing

AI optimization agents for supply chain

LLMs and optimization, together, to recover the value lost across your supply chain.

[01] Examples

Start with the decision that costs you most.

A few places clients start. The agent works on any decision your network data can describe: ask as you would ask a team of consultants.

01
Network design
WAREHOUSES · FLOWS · SOURCING What is the impact of multi-sourcing on the service level, keeping the transportation cost per item fixed?
02
Stock reduction
PER-SKU SAFETY STOCKS, AI-DRIVEN Which safety stocks can come down without losing a point of service level?
03
Customs optimization
TARIFFS · SPLIT RULES Which trade agreement applies to this shipment, and what does optimizing duties do to stock and service?
04
S&OP
DEMAND · SUPPLY · CAPACITY Can the network absorb next quarter's plan, and at what cost?

[02] How it works

Generative AI captures the constraints and knowledge that never reach a ticket. Deterministic optimization turns them into decisions you can defend.

01 · CAPTURE
Knowledge, not just data

The agent reads your systems and asks your teams. The constraints that never made it into a ticket go into the model.
02 · OPTIMIZE
Numbers you can defend

Deterministic optimization estimates impact on service level, cost per item and stock, down to a single package.
03 · ITERATE
A living model

When the network changes or an assumption is too simple, tell the agent. The model is rebuilt and the numbers move.

[03] Around the decision

The same agents handle the repetitive work between your tools: reading emails, filling ERP and APS fields, updating files, building dashboards.

AGENT LOG · LIVE 37 TASKS TODAY

06:12EMAIL ⟶ ERPRead a carrier delay notice, updated the ETA on PO 4500-2231RUNNING
06:09WMS ⟶ TEAMSPosted the morning backlog summary to the logistics channelDONE · 8 S
06:06TEAMS ⟶ ERPCreated a transfer order from a planner's message in TeamsDONE · 11 S
06:03ERP ⟶ APSSynced 112 changed lead times from supplier master dataDONE · 33 S