Three live AI products that hit a wall on cost or load — and the specific architectural changes that flattened the bill and kept them fast. Same lens we bring to every build.
An AI calorie-tracking app whose traffic spikes three times a day — breakfast, lunch, dinner. The backend was sized for the average minute, not the meal-time peaks that actually mattered. We split food-recognition behind a cache and autoscaled to the real curve.
Read the full case study →A personalised social platform that re-called the model on every feed refresh — so cost scaled linearly with engagement, the opposite of what you want. Caching identical generations and routing simple requests to cheaper models flattened the spend while the audience kept growing.
Read the full case study →A real-time AI voice and calling product where latency is the product — a slow inference path meant dropped calls and churned users. Right-sizing the infrastructure and streamlining the inference pipeline kept it fast and stable under real concurrent load.
Read the full case study →Predictable usage spikes, cost that scales with engagement, latency that churns users — these are exactly what a build audit is for. We'll map where yours breaks first.
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