Beyond dashboards. Into optimal decisions.
This is a live walkthrough of what we build for F&B operations teams. Real data, real constraints, real Linear Program — watch the solver close a $40k gap that traditional planning misses every year.
Meet Cascade Foods
A mid-sized F&B manufacturer in the Pacific Northwest. Ten product lines, six raw materials, one planning team deciding each month: how much to buy, when, and how much to produce.
Four views. One complex system.
Before the optimizer, understand what it's working with. Click any tab to explore the demand landscape, ingredient prices, what goes into each product, and the capacity picture.
A $40k gap hiding in plain sight.
The traditional planning approach: buy ingredients when you need them. Rational, simple, wrong — when ingredient prices follow seasonal cycles that are out of phase with product demand cycles.
This was Cascade Foods. Now imagine it on your data.
We build and deploy production-ready LP and MILP optimization models for operations teams. From demand planning to procurement to production scheduling — running on AWS, GCP, or Azure.
No deck. No pitch. 45 minutes on your actual planning problem.
# Local test pip install fastapi uvicorn pyomo highspy uvicorn api:app --reload --port 8000 # Cloud (AWS ECS / GCP Cloud Run / Azure Container Apps) # See Dockerfile + deploy-gcp.sh / deploy-azure.sh / aws-ecs.yml # Update API_URL in lib/constants.ts before deploying
This page works as a static file with zero dependencies. The live solver only runs on Re-run clicks.