Service

Performance Audit for predictable speed

Baseline + bottleneck identification focused on real user journeys backed by measurable evidence.

Promise

Baseline metrics you can track (p95/p99, error rate, slowest pages/endpoints)
Findings tied to journeys and endpoints (not vague guesses)
Prioritized optimization plan (quick wins + high-impact fixes)

What you get

Baseline & metrics report

Key pages/endpoints, p95/p99, error rate, and performance trends.

Bottlenecks & root-cause hypotheses

Top slow points + likely causes (API/DB/query/render/third-party) with “where to look” guidance.

Action + validation plan

Prioritized fixes (impact/effort), owner suggestions, safe test plan, and a re-test checklist to confirm improvements.

How it works

Critical journeys first (login, checkout/payment, approvals, search, key dashboards)
Define performance expectations (p95/p99 goals, error rate) + constraints (env/data/traffic patterns)

Evidence you will actually see

Baseline snapshot: p95/p99, latency + error rate + slowest endpoints.
Top bottlenecks ranked by impact
Before/after comparison template for engineers
Constraints notes (data, environment, third parties)

Evidence over opinions: every claim is backed by measured runs.

Tools & stack

Load & performance testing (k6 / JMeter)

Baselines for key journeys, p95/p99, throughput, and error rate under realistic traffic.

Frontend profiling (DevTools / Lighthouse)

Identify render/JS/asset bottlenecks and user-perceived latency (CWV signals when relevant).

API & endpoint diagnostics (Postman / curl)

Reproducible endpoint measurements, payload checks, timeout patterns, integration behavior.

Backend & DB investigation (SQL checks)

Validate slow queries, N+1 patterns, data consistency signals, and “is it the DB?” confirmations.

Monitoring & tracing (APM: Datadog / New Relic / OpenTelemetry)

Correlate slow requests across services, dependencies, and third parties with trace evidence.

CI execution & reporting (Jenkins + Git workflows)

Repeatable runs, PR-friendly evidence, and “before/after” comparisons after changes.

FAQs

What do you measure in a Performance Audit?+
We baseline real journeys with p95/p99, throughput, and error rate—plus the slowest pages/endpoints.
How do you choose which journeys to test first?+
We start with release-critical flows (login, checkout/payment, approvals, search, key dashboards).
Do you test UI performance or API performance?+
Both—UI for user-perceived latency and API/DB for the underlying bottlenecks.
How do you isolate where the bottleneck is?+
We triage by layer (UI vs API vs DB vs third-party) and validate with reproducible runs.
Will this impact production traffic?+
Not by default—we use safe test plans, controlled load, and staging where possible.
What do engineers actually get at the end?+
A baseline report, a ranked bottleneck map with likely causes, and a prioritized action plan.
How do you prove improvements after fixes?+
We re-run the same scenarios and provide before/after comparisons using the same metrics.
How long does a typical audit take?+
Small scopes can be quick; larger systems take longer—but we deliver value early with a baseline + top bottlenecks first.
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