Adageis
A healthcare technology company committed to revolutionizing patient care through its patented fintech AI platform, which is designed to optimize revenue cycle management for healthcare providers.
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problem
The application experienced increasing performance bottlenecks as user traffic and data volume grew, resulting in high p95 query latency, elevated CPU utilization, and degraded user experience.
The Supabase-backed PostgreSQL datastore contained unoptimized high-volume tables and inefficient query patterns that caused slow read paths and unnecessary compute overhead.
Additionally, the lack of systematic performance benchmarking and shared optimization practices limited the team’s ability to proactively identify bottlenecks, validate improvements under real-world load, and maintain long-term backend reliability and scalability.
solution
Optimized a Supabase-backed PostgreSQL datastore to reduce p95 query latency by 30% and CPU usage by 20%, by partitioning high-volume tables, introducing composite indexes, rewriting the top slow queries using CTEs, and caching hot read paths based on access patterns.
Strengthened backend reliability and team efficiency by validating performance under load, benchmarking optimizations, and guiding shared ownership and best practices.
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