AI assistant for supply chain operators

Project

Tasks

• Discovery & architecture
• Core platform build
• Integrations & automation
• Launch, enablement & operate

KPIs

1. 1. Production — Grade delivery
2. 2. Phased — Rollout model
3. 3. Operated — Post-launch support

Project journey

1. 1. Discovery & architecture

   Mapped current-state processes, integration points, and non-negotiable constraints. Produced a technical design and rollout plan that respected existing systems of record.

2. 2. Core platform build

   Engineered primary user journeys, role-based access, and data models so day-to-day work could run on one coherent application rather than parallel tools.

3. 3. Integrations & automation

   Connected upstream and downstream systems, automated repetitive handoffs, and added exception queues where human review still mattered.

4. 4. Launch, enablement & operate

   Rolled out with training, observability, and operated support so adoption stuck and incidents had clear response paths.

Challenges

AI Supply Chain Assistant required a production-grade platform that could replace fragmented tools and manual coordination. Teams were losing time reconciling data across systems and could not trust a single operational view when volume spiked.

• Fragmented workflows spread work across spreadsheets, inboxes, and legacy tools that did not share a source of truth.
• Manual handoffs introduced delays and errors whenever ownership moved between teams or channels.
• Limited visibility made it hard for leadership to see exceptions before they became customer or compliance issues.
• Scale pressure exposed tools that worked for pilots but failed under real concurrent load.

Results