I built Cache.Today I implemented a caching layer for the EV charging planner to avoid recomputing identical analyses. Previously, if two users ran the same configuration (for example, Top 10 sites in South Delhi using Demand + Grid scoring), the entire ML, grid, and solar pipeline would run again even though the output was identical.To solve this, I created a cache key by hashing the parameters that actually affect the results: number of sites, scoring mode, area filter, minimum grid threshold, and model version. Before running an analysis, the application checks Supabase for an existing cached result. If found, it instantly returns the stored results and GeoJSON map data instead of recomputing everything.I also included the model version in the cache key so that retraining the CatBoost model automatically invalidates old cache entries. To prevent stale infrastructure data from being served forever, cached entries expire after one week.Additionally, I added a lightweight session-level cache for map interactions, reducing repeated calculations when users switch between recommended sites.The result is a shared, persistent caching system that reduces analysis time from roughly 3.5 seconds to a few milliseconds for repeated scenarios while remaining completely transparent to users