Performance and load testing for organisations launching or changing systems under real demand. PinnacleQM measures capacity, response times and resilience, and explains the causes of slowdowns before go-live.
30+years of international
IT experience
Systems that pass functional testing can still fail under real load: pages time out at peak, batch jobs overrun and integrations queue up. These failures happen in public, on launch day or at month-end, when fixing them is hardest.
PinnacleQM plans performance testing around real usage and business peaks, runs load, stress and endurance tests, measures end-user response times and uses monitoring to find the root cause, not just the ceiling. Recommendations are practical.
A load test that only reports failure points leaves you guessing. We combine realistic load with diagnostics so every result comes with an explanation and a fix.
Measured by virtual testers during execution.
Tests reflect real user journeys, volumes and business peaks, not arbitrary numbers. Models are agreed with business and technical owners.
Server, network and latency monitoring help explain why performance degrades. Findings point to the tier that needs tuning.
Virtual testers capture real response times for key transactions as users experience them. Timings reflect real devices, browsers and networks.
Enigma scales obfuscated data so tests run against realistic volumes. Personal information is never exposed.
Baseline timings in regular regression catch slowdowns long before go-live. Trends are visible release by release.
Reports state limits, risks and tuning actions in plain business terms. Leaders know how much growth the system can absorb.
From workload modelling to tuning advice, across web, mobile, API and enterprise systems.
We analyse production usage, business peaks and growth plans to build workload models that reflect how real users and batch processes load your systems. Models are reviewed with business owners before testing begins.
We run load, stress, spike and endurance tests under realistic conditions to find capacity limits and breaking points, and to show how response times change as load rises and as tests run over time.
We integrate server, network and latency monitoring during tests, so bottlenecks in code, database, infrastructure or integrations are identified and explained. Findings point teams to the exact component that needs attention.
UXperform and virtual testers capture end-user response times for key transactions across systems and devices, showing performance as users experience it. Slow journeys are reported with device and location detail.
Enigma scales production data sets for performance and scalability testing and protects them with smart obfuscation, so volumes are realistic and safe. Data volumes match what production will really hold.
We add response-time baselines to regular automated regression, so performance changes are detected in every release rather than once a year. Alerts show when a release makes key transactions slower.
We follow K-SPARC, a structured five-phase performance engineering method that moves from business objectives to proven capacity and clear tuning advice.
Investigate requirements, performance goals, SLAs and business objectives before any test design begins.
Build workload models, test plans, tool selection and resource allocation from your actual traffic analytics.
Profile baseline application behaviour, set benchmark thresholds and review the feasibility and risks of the plan.
Design realistic scripts, scenarios, user groups and test data using K-SPARC accelerators and industry tools.
Execute with real-time monitoring, find bottlenecks, then report capacity, database and infrastructure recommendations.
HealthTest obfuscated production records, created full test condition variations and tested more than 5 million transactions per day, without exposing any patient information along the way.
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Enginuity as a service and a team of 12 replaced a 125-person offshore regression team across a Big 4 bank's end-to-end lending systems.
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A state health integration re-platforming was delivered by 7 testers in 12 months against competitor estimates of 40 to 46 testers over 36 months.
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7 min read
Why load tests show where systems fail but not why, and how to add diagnostics that explain the result.
7 min read
How differences between test and production environments distort performance results, and how to correct for them.
7 min read
The evidence, residual risks and conditions a credible go-live recommendation must include for decision-makers to rely on it.
Tell us about the system, expected users and peak periods. We will outline a performance test approach that answers how much load you can handle and why.
Mention the system, expected peak users and your go-live date.
