Load modeling & testing
Realistic workload models from your actual traffic patterns — not synthetic uniform load — executed as load, stress, soak, and spike tests.
Quality Engineering
Find your breaking point before your customers do
Systems rarely fail at average load — they fail at the sale, the launch, the Monday-morning spike. Performance engineering means knowing your saturation point, your degradation curve, and your recovery behavior before real traffic teaches you.
Downtime during peak demand is revenue lost at the exact moment revenue was highest — plus the engineering week that follows, spent firefighting instead of shipping. Slow is almost as expensive: users abandon what lags.
What we do
Bottleneck analysis
We don't stop at 'it got slow at 400 RPS.' Profiling across app, database, and infrastructure to name the constraint and the fix.
Scalability validation
Does autoscaling actually scale? Horizontal scaling behavior, warm-up costs, and failure recovery tested under load.
Production monitoring
Dashboards and alerting tied to user-experienced latency and error budgets — so regressions surface as signals, not support tickets.
How it’s delivered
01
Model
Define workloads, SLOs, and the questions the test must answer.
02
Script
Build the scenarios and data at production-like scale.
03
Execute & analyze
Run, profile, and identify constraints — with your engineers in the loop.
04
Verify
Re-test after fixes; baseline the result for the next release.
Tools & standards
- Load generation
- JMeter, k6, Gatling
- Profiling & observability
- APM tooling in your stack (Grafana, Prometheus, CloudWatch, or equivalent)
What you receive
- Workload model and executable performance test suite
- Bottleneck analysis naming the constraint, not just the symptom
- Capacity statement: what load you can take and where it breaks
- Performance baselines tracked release over release
Who this is for
- Teams with a launch, sale, or seasonal peak on the calendar
- Engineering leaders who've been surprised by production slowdowns twice
- Products moving to cloud or microservices where old capacity intuitions no longer hold
Where is your QA today?
Walk through your suite, coverage, and release cadence with a QE lead.