K-SPARC is a structured performance engineering framework for systems facing real demand. It covers load, stress, endurance, scalability and spike testing and capacity planning, with repeatable results tied to your SLAs.
30+years of international
IT experience
Most organisations discover bottlenecks in production, during end-of-month surges, product launches or seasonal peaks. Performance testing is bolted on late, capacity is guessed rather than measured, and SLA breaches go undetected until customers feel them.
K-SPARC replaces ad hoc load tests with a structured five-phase method. It starts from business objectives and SLAs, builds workloads from real traffic, finds bottlenecks during execution and delivers capacity plans for the next 12 to 24 months.
Unlike ad hoc load testing, K-SPARC produces repeatable, SLA-aligned outcomes, from baseline profiling through to infrastructure sizing and capacity planning.
Proven on complex enterprise programmes.
Every test is mapped to business objectives and agreed SLAs, so results answer the questions leaders actually ask.
Workloads are built from actual traffic analytics and concurrent user patterns, rather than arbitrary virtual user numbers.
Real-time monitoring during execution finds bottlenecks as they appear, lowering rework and avoiding delivery delays.
Slow queries are identified and tuned, because database behaviour is one of the most common causes of degradation.
Capacity models guide infrastructure sizing for 12 to 24 months of business growth, based on measured data.
Cloud load generation simulates realistic, geographically distributed users, from thousands to millions where the business genuinely needs it.
K-SPARC covers every dimension of application performance risk in a single structured engagement.
We simulate expected user loads to confirm behaviour under normal and peak operating conditions, verifying response times, error rates and resource use against the SLAs you have agreed with customers and regulators.
We push systems beyond designed capacity, often to several times the expected load, to find breaking points, measure how gracefully they degrade and validate recovery before your users discover the limits.
We run sustained load for extended periods, 72 hours or more where required, to expose memory leaks, resource exhaustion and the gradual degradation that short tests never reveal before go-live.
We evaluate how systems handle growing traffic and data volumes, validating horizontal and vertical scaling and auto-scaling behaviour, and setting the thresholds at which scaling should take place under real demand.
Concurrent user analysis, database query profiling and hardware recommendations give data-driven infrastructure requirements for 12 to 24 months of growth, so capacity and budget decisions rest on evidence rather than estimates.
We validate behaviour under sudden, dramatic increases in load, such as flash sales, breaking news, viral surges or scheduled batch processing, and confirm the system stays available and recovers cleanly afterwards.
A five-phase performance engineering method that detects bottlenecks early, lowers rework and gives you repeatable results aligned to your business SLAs.
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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Why load tests show where systems fail but not why, and how to add diagnostics that explain the result.
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How differences between test and production environments distort performance results, and how to correct for them.
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The evidence, residual risks and conditions a credible go-live recommendation must include for decision-makers to rely on it.
Tell us about your system, expected peaks and SLAs. We will scope a K-SPARC engagement that gives you data-driven capacity confidence before your next launch, surge or seasonal peak.
Mention the system, expected peak users and key dates.
