The problem
Demand planning was based on lagging spreadsheets and intuition. The quoting workflow required staff to search across an unwieldy catalog under time pressure.
The result was over-provisioned inventory, slow quoting, and too much variance between advisors.
What we built
We implemented a demand forecasting model over historical usage data, then wrapped the quoting workflow in a retrieval and re-ranking layer designed for advisor speed, not demo polish.
The system exposed confidence levels and fallback paths so staff knew when to trust the machine and when to escalate.
The hand-over
The ops team received monitoring dashboards, model drift checks, and a concise runbook for exception handling.
The platform team now owns the workflow without relying on us for day-to-day tuning.