Tepia profiles and fixes slow apps: queries, payloads, rendering and startup, with proof in numbers. Measurable gains in 4 to 8 weeks.
Tepia makes slow software fast: profiling real bottlenecks, fixing queries, payloads and rendering, and proving the improvement with numbers users feel. Most focused engagements show measurable gains within 4 to 8 weeks.
Tepia instruments real user timings before touching code, so effort goes to bottlenecks users actually feel.
Read: Database Design & Management
Every fix ships with a before and after measurement on the same scenario, on real devices and real networks.
Read: App Evolution Consulting
Database, API, frontend, mobile and infrastructure are profiled together, because slow screens usually blame the wrong layer.
Read: DevOps & CI/CD
Performance budgets in CI and regression alerts defend the gains after the Tepia engagement ends.
Read: App Maintenance and SupportIndustry figures Tepia plans around when scoping performance work.
Around half of mobile users abandon experiences that load slowly, a finding Google has published repeatedly.
Tepia clients report apps averaging 4.5 stars, and responsiveness is one of the steadiest drivers of ratings.
Faster experiences convert meaningfully better, with studies from Google and Deloitte tying sub second gains to revenue.
Nobody files a ticket that says the app feels heavy. They just open it less. Slowness shows up as churn, abandoned carts and support calls about things that work, eventually. Tepia treats performance as a product feature with a number attached: how long the key screens take, for real users, on real networks and mid range phones.
The discipline is simple and rarely followed: measure before touching anything, fix the biggest offender, measure again. Guessing is how teams spend a month optimizing something users never noticed.
| Layer | Common culprit | Typical fix |
|---|---|---|
| Database | Missing indexes, N+1 queries | Indexing, query rewrites, caching |
| API | Oversized payloads, serial calls | Trimmed responses, batching, pagination |
| Web frontend | Bundle weight, render blocking | Code splitting, lazy loading, image discipline |
| Mobile app | Startup work, main thread jank | Deferred initialization, list virtualization |
| Infrastructure | Undersized or misconfigured services | Right sizing, autoscaling, CDN placement |
Tepia profiles across all five layers before fixing any of them, because the slow screen is usually blamed on the wrong layer.
Performance work that ends with a fast week and a slow quarter has failed. Tepia leaves guardrails: performance budgets checked in CI, dashboards on the key user timings, and alerts when a release regresses them. Speed becomes something the pipeline defends, not something that erodes until the next rescue.
Tepia clients report apps averaging 4.5 stars on the App Store and Google Play, and responsiveness is one of the most consistent drivers of ratings and retention.
Discovery instruments the product and gathers two weeks of real timings across devices and networks, producing an Investigation Summary that ranks bottlenecks by user impact. The fixes then run as short Development cycles, each ending with a before and after measurement on the same scenario.
Testing includes load testing where scale is part of the problem, and Training walks your engineers through the profiling setup so they can keep diagnosing. Support remains available, and the CI budgets stay with you. A US based Tepia project manager runs the engagement end to end.
Call when users mention slowness, when dashboards show sessions shortening, or when the team has stopped opening the old admin because it hurts. Most focused engagements show measurable, user felt gains within 4 to 8 weeks, with the biggest wins usually in the database and payload layers.
Performance pairs naturally with database design and management and DevOps and CI/CD, where the guardrails live.
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