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Nine capabilities across three disciplines. Delivered as a full practice build, a targeted assessment, or an embedded senior engineer inside your team.
Regression that runs itself,
on every deployment.
Frameworks built to be owned by your team after we leave. Readable scenarios, stable selectors, deterministic data, and a maintenance cost that does not grow faster than the product.
- BDD framework engineering
- C# with Playwright and Cucumber, or the stack your team already runs. Gherkin scenarios that a business analyst can read and a build agent can execute. Built from zero on a supermarket e-commerce replatform to cover the entire checkout pipeline across four payment methods and two gateways.
- API & GraphQL automation
- Full cart-to-order and request-to-settlement lifecycle coverage. Pricing calculations, state transitions, fulfilment logic and contract validation — the layer where defects are cheapest to catch and most often missed.
- CI/CD quality gates
- Suites wired into Azure DevOps, Jenkins or GitHub Actions so regression is a promotion condition rather than a calendar event. On one engagement this cut the feedback loop from days to minutes.
Load models built from
production, not imagination.
Anyone can generate traffic. The value is in a workload model that genuinely represents what your users do — and in non-functional requirements written precisely enough to be failed.
- Workload modelling & capacity planning
- We derive the model from real telemetry: top flows by volume, realistic think times, correct data cardinality, honest concurrency. On a core banking platform this produced 2 million transactions per execution — roughly a quarter of live volume — as a statistically defensible baseline for release governance.
- Full-spectrum execution
- Baseline, load, capacity, stress, spike, endurance and soak. JMeter, LoadRunner, NeoLoad, Gatling and NBomber. Distributed injection across VPN-secured cloud environments with coordinated result aggregation.
- NFR definition & validation
- Turning “it should be fast” into thresholds with a number, a percentile and a load condition attached. Then proving the platform meets them — or documenting precisely where it does not, which is often the more useful outcome.
If you cannot see the
regression, you will ship it.
Instrumentation is not a dashboard exercise. Done properly it is the difference between finding a defect in an hour and finding it in a customer complaint.
- APM platform rollout
- Dynatrace, New Relic, Splunk, Elasticsearch and Kibana, Grafana, CloudWatch, Azure Monitor. Deployed with the alerting, escalation and on-call routing actually configured. An enterprise rollout we assured surfaced 29% of production defects and 35% of UAT defects across 167 test cases.
- Automated telemetry comparison
- Built in-house on a banking engagement: pull performance data for any two executions, auto-compare, publish a structured HTML regression report. Hours of manual post-test analysis removed from every release cycle.
- Environment health & test integrity
- Most “flaky” automation is not flaky — it is an unhealthy environment lying to you. We instrument the environment itself so a red test means a real defect, which is the only way a suite keeps its credibility.
Assessment
Two to three weeks. We build the workload model, instrument a baseline and hand you a ranked list of what fails first, with evidence. Often the highest-value thing we do.
Practice build
We stand up the framework, the pipeline integration and the reporting, then transfer it. Includes a Testing Centre of Excellence model where the organisation needs standards across many delivery teams.
Embedded senior
One senior engineer inside your team for a defined engagement. No juniors on the invoice, no bench to feed, no discovery phase you pay for twice.
Bring us the release you are
least comfortable shipping.
A short, specific conversation is usually enough to tell whether we can help. No discovery deck, no bench to feed — just an engineer who has broken systems like yours on purpose, many times.
Typical first engagement: a two to three week assessment producing a workload model, an instrumented baseline and a ranked list of what will fail first.