Artifact: MVP Scope Cut — Kestrel Smart Scheduling Suite

A 12-feature idea cut to an MVP, with a cheaper engineer-proposed alternative -- exercises co-12, co-06, co-26. Kestrel is a fictional product; every quoted number, question, or finding here is an illustrative, constructed example, not real data or a real transcript.

Original 12-feature scope: full ML-based constraint solver, historical-pattern learning, multi-location optimization, staff-preference weighting, real-time re-optimization on cancellations, and seven more sub-features.

MVP, testing the riskiest assumption (value risk, now at real-usage scale, not a 5-manager pilot): a single-location, rule-based "suggested schedule" that pre-fills the existing editable grid using availability + max-hours constraints only, badged "Suggested" in the UI so managers know it's a recommendation they can freely override.

Engineer's cheaper, ~80%-value alternative: instead of a full ML pipeline (an estimated 6 engineering-weeks), build a simple greedy constraint-satisfaction scheduler (an estimated 1 engineering-week) that respects the same availability and max-hours rules. It won't learn from history or optimize across locations, but it captures most of the concierge pilot's demonstrated value -- a pre-filled, editable starting point instead of a blank grid -- at roughly a sixth of the cost.