Rebuilt a Streamlit prototype into a real product: an optimizer that respects queue, skill, and demand constraints instead of a spreadsheet someone fixes by hand every week.

The scheduler builds agent shift schedules with an OR-Tools CP-SAT model over queue, skill, and demand constraints, then exposes them in a drag-to-edit calendar with Erlang C volume forecasting, shift swaps, time-off handling, and scenario comparison.

Tuned against a 175-agent roster it reaches 99.4% shift-floor fill, 97%+ per-queue coverage, and zero unassigned shifts within a 420-second solve budget; splitting the solve into four parallel shards cut runtime from 162s to 42s. The v2 build shipped with 44 API endpoints and 161 automated tests.

• → Real authentication before live employee data
• → Forecasting from a live ACD feed
• → Scenario-compare polish

• ! Demo data only so far