Performance engineering
We help engineering teams model real workloads, find constraints, and build repeatable performance evidence into delivery—so capacity and release decisions rest on what the system can demonstrate.
Requests
1.48k/s
P95 latency
286 ms
Errors
0.18%
Response under increasing load
Latency remains within the example threshold
12 min run
The chart demonstrates reporting format only. It does not represent a client result.
Performance work should answer a decision, not just produce a test report.
Can we handle the next event? Which change caused the regression? Where will growth create pressure? Is this release safe to progress? We begin with the question, then build the smallest credible body of evidence needed to answer it.
What we deliver
The work connects business demand with system behaviour. Your team gets test assets, findings, and decision criteria it can keep using after the engagement.
Translate production traffic, customer journeys, seasonality, data, authentication, and dependency behaviour into scenarios that exercise the system you actually operate.
Journey mix · concurrency · arrival rates · test data
Measure steady load, peaks, spikes, endurance, and recovery. Establish where service levels begin to degrade and how much usable headroom remains.
Latency · throughput · errors · saturation · recovery
Correlate test results with application and infrastructure signals to narrow the search from “the platform is slow” to the components and conditions worth investigating.
Traces · metrics · logs · configuration · dependencies
Turn stable scenarios into automated k6 or Gatling checks, with thresholds and reports that fit pull requests, progressive delivery, scheduled runs, and release decisions.
CI/CD gates · baselines · trends · release summaries
A practical engagement
Define the business event, release, growth question, or recurring risk the work needs to answer.
Select critical journeys and build a credible demand model from available evidence and operational knowledge.
Run controlled tests, observe the whole system, validate findings, and separate symptoms from causes.
Leave the team with reusable scenarios, agreed thresholds, clear reporting, and a practical operating rhythm.
Focused assessment
Clarify a risk, review existing evidence, and define the next testable step.
Targeted engagement
Prepare for a release, migration, launch, campaign, or known capacity concern.
Ongoing capability
Establish tooling, quality gates, baselines, and ownership within the team.
Recommendation
“Michal played a key role in creating dashboards and tools that improved live monitoring capabilities and streamlined performance testing across pre-release and production environments.”
Related insight · 24 August 2026
A practical case for turning load testing from an occasional project into a recurring signal that catches gradual performance regressions.
Read the articleStart with the question
Bring the release, event, growth target, or reliability concern. We’ll identify the evidence that would make the next decision clearer.