Quality you can measure, not hope for.
Test automation, performance and security testing built into the pipeline — so every release carries its own evidence.
- Audit
- Foundation
- Coverage build-out
- Operate & transfer
The shape of the test suite matters.
Manual regression testing is a tax that grows with every release — and it still misses things. It has a second cost that is harder to see: because the test pass takes a week, releases get bundled, and because releases are bundled, each one carries more risk, which makes everyone want a longer test pass. The queue at the end of delivery is usually the reason the organisation ships quarterly, and no amount of effort inside that queue fixes it.
We build testing as an engineering system: fast automated suites in CI, exploratory testing where humans excel, performance and security checks before users find out. Tests are written by the engineers who write the code, in the same repository, reviewed the same way, and they run on every change rather than at the end of a phase. The measure that matters is not coverage percentage — it is the escape rate, the number of defects that reach production and what they had in common.
The pyramid matters: many fast unit tests, fewer API tests, a thin crust of end-to-end — inverted pyramids are why test suites get deleted. A suite that takes an hour and fails for unrelated reasons is not a safety net; it is a toll booth people learn to route around.
Four capabilities, built in this order.
Automation first, because everything else depends on a suite people trust. Performance and security gates come next, and skilled exploratory testing is what you spend human attention on once the machine handles the repetitive part.
Automation architecture
Unit, API and E2E suites proportioned properly, running in CI, fast enough that nobody is tempted to skip them. The architecture decides where each behaviour is tested once — business rules at the unit level, contracts at the API level, and only the journeys that earn it end to end. Test data is generated and isolated per run, because shared fixtures are the most common reason a suite becomes unreliable as it grows.
Performance testing
Load profiles from real traffic, k6 or Gatling scenarios, and regression gates on the latencies that matter. We model the peak from your own access logs rather than from a round number, break the system deliberately in rehearsal to find where it gives way, and then set a gate so a future change cannot quietly undo the fix. Results are reported at the percentile users actually feel, not as an average.
Security testing
SAST and DAST in the pipeline plus dependency and secret scanning — the routine hygiene that prevents routine breaches. Findings are triaged with a severity policy agreed up front, so the pipeline blocks on what matters and files the rest rather than producing a wall of red that everyone learns to ignore. Deeper work — threat modelling, penetration testing — pairs this with our Cybersecurity service.
Exploratory & release QA
Skilled humans probing what automation cannot: workflows, edge states, and the “that’s odd” instinct. Sessions are charter-based and time-boxed, with notes that turn a finding into either a defect or a new automated test. This is where experienced testers earn their place — an automated suite checks what you already thought of, and exploratory testing is how you find out what you did not.
How the work runs, day to day.
Proportions decide whether a suite survives.
Every failing test suite we have inherited had the same shape: a thin base of unit tests, almost nothing at the API level, and hundreds of browser tests carrying the entire burden of proof. That shape is slow, brittle and expensive to diagnose, so it is eventually disabled “temporarily”. We rebuild the proportions deliberately. Business rules are tested where they live, in milliseconds. Contracts between services are tested at the API level, where a failure names the culprit. End-to-end tests are reserved for a small number of journeys that must never break, and each one has to justify its runtime. The target is a full suite in CI that stays within a developer’s patience.
- Each behaviour tested once, at the cheapest level that can prove it
- Contract tests between services so an integration break is caught before the environment is assembled
- Test data generated and isolated per run — no shared fixture that three suites quietly depend on
- Suite runtime treated as a budget, with the slowest tests reviewed like any other performance problem
A flaky suite is worse than no suite.
The moment a red build might mean nothing, the suite stops being evidence. People re-run it, then ignore it, then merge past it — and the organisation is back to manual testing with an expensive extra step. So flakiness is handled as a policy rather than a mood. A test that fails intermittently is quarantined on first flake so it stops blocking the pipeline, and it is fixed or deleted within the sprint. Deletion is a legitimate outcome: an unreliable test that nobody will fix is a liability, and pretending otherwise is how suites rot. Quarantine counts are visible on the quality dashboard, so the debt cannot accumulate quietly.
- Quarantine on first flake — the pipeline stays trustworthy while the fix is scheduled
- Fix-or-delete inside the sprint, with the decision recorded
- Retries never used to hide non-determinism; the cause is found instead
- Quarantine count reported alongside coverage and escape rate, so nobody can look away from it
Break it in rehearsal, so the peak passes quietly.
Performance work fails when it starts from a target nobody derived. We build the load profile from your own traffic — the mix of endpoints, the ratio of reads to writes, the shape of the daily and seasonal peak — and then push past it until something gives way, because the useful information is where the system breaks and what breaks first. Usually it is a database connection pool, a chatty query, or a downstream service with no timeout. Each finding is fixed and then pinned by a regression gate in the pipeline, so the improvement survives later feature work. The rehearsal is repeated until the peak holds no surprises.
- Load profile modelled from real traffic, including the write mix, not just page views
- Soak and spike tests as well as steady load — different failures live in each
- Latency reported at p95 and p99, with the slow path traced rather than guessed
- Regression gates on the journeys that matter, so a future change cannot silently undo the work
Escape analysis turns incidents into tests.
Coverage percentage is a weak signal — it tells you what was executed, not what was checked. The number we manage is the escape rate: defects that reached production, classified by what they had in common and by the level at which they should have been caught. Every escape produces a test at that level, and the pattern across escapes tells you where the risk map is wrong. This is also how a suite gets built on a codebase that has none: characterisation tests around the flows that hurt when they break, then coverage that grows with every change rather than in a separate quality project nobody funds twice.
- Every production defect classified: what missed it, and at which level it should have been caught
- A test written at that level before the fix is closed
- Risk map updated from the pattern, not from opinion about which modules feel fragile
- Escape rate trended over time
What each side of the table gets.
A suite fast enough to run on every change.
Developers get tests that live beside the code, run quickly, and fail for a reason they can read. Contract tests catch integration breaks before an environment is assembled, and the quarantine policy means a red build still means something. Writing tests stops being a separate task at the end of a ticket and becomes part of how the change is made.
- Full suite inside an agreed CI runtime budget
- Failures name the level and the likely cause
- Test data generated per run, isolated by default
A release that carries its own evidence.
Every candidate arrives with its test results, performance comparison and scan output attached, so the release decision is a reading rather than a negotiation. Exploratory sessions are charter-based against the changes in the release, and their notes become either defects or new automated tests. Nothing ships without automated evidence.
- Release report generated by the pipeline, not assembled by hand
- Exploratory charters targeted at what actually changed
- Known issues listed with severity and a decision, not discovered later
Hygiene that runs on every commit.
Static analysis, dependency and container scanning, secret detection and dynamic testing against a running build are pipeline stages with an agreed severity policy. Critical findings stop the line; the rest are filed with an owner and a date. The value is routine: many breaches begin with something ordinary that nobody was watching for, and a scan that runs on every commit is how ordinary things get noticed.
- SAST, DAST, dependency, container and secret scanning as pipeline stages
- Severity policy agreed up front so the gate is credible
- Findings tracked as work, with the trend reported rather than the raw count
Quality as a trend, not an argument.
The quality dashboard reports escape rate, coverage trend, suite runtime and quarantine count in one place, updated by the pipeline. It answers the question executives actually ask — is this getting better — and it makes the cost of skipping the work visible. It also removes the recurring argument about whether QA is a bottleneck, because the numbers show where delivery time is going.
- Escape rate as the headline measure, trended over time
- Suite runtime and quarantine count reported alongside coverage
- Release frequency tracked against the quality trend, so the trade-off is explicit
What every engagement includes, and what depends on your codebase.
Four phases, then your engineers own it.
Audit
Current coverage, escape analysis (what bugs reached production and why), and the risk map. We read recent incidents and defects rather than relying on where the team feels fragile, measure what the existing suite actually costs in time and trust, and produce a ranked list of where a test would have paid for itself. The risk map that comes out of this phase drives everything after it.
Foundation
CI test infrastructure, the first high-value suites, and flaky-test quarantine discipline. Test data generation, environment isolation and the reporting that makes a failure readable land first, because they decide whether the suite grows or rots. The first suites go around the highest-risk flows on the map, which is also how a codebase with no tests gets its characterisation layer.
Coverage build-out
Suites grown along the risk map; performance and security gates added to the pipeline. Coverage expands with the work rather than in a separate project — every change carries its tests, and every escape adds one. The load profile is modelled and rehearsed in this phase, and the gates that pin the results are switched on once the baseline is stable.
Operate & transfer
Quality dashboards, escape-rate tracking, and your engineers owning the tests they ship with. We pair through the handover, agree the policies that keep the suite honest — runtime budget, quarantine rule, severity thresholds — and stay on the bench for the peaks. Where a client prefers it, a QA team stays embedded, but the design assumes independence.
What you have at the end.
- Automated test suites running in CI with coverage reports
- Performance test scenarios and latency baselines
- Security scanning integrated into the pipeline
- Flaky-test policy and quarantine tooling
- Quality dashboard: escape rate, coverage trend, suite runtime
- A risk map tied to real incident history, kept current
- Exploratory charters and session notes your testers can carry on running
When this is the right call.
Choose this when the release date is set by a test pass rather than by the work, when the same class of defect keeps reaching production, or when a red build no longer means anything to anybody. Choose it before a known peak — a campaign, a seasonal load, a regulatory deadline — while there is still time to break the system in rehearsal and fix what gives way. It is also the right first move when you have inherited a codebase you are afraid to change: characterisation tests are what make Legacy Re-engineering or a modernisation programme safe to start. It pairs closely with DevOps & Cloud, since a trustworthy suite is what lets deploys become routine.
It is the wrong investment when the product has not found its shape yet. A team still testing whether anyone wants the thing should ship an MVP with sensible foundations and add depth once the answer is yes; heavy end-to-end coverage over a design that will change next month is work that gets deleted. It is also the wrong call if the organisation wants a coverage number to report rather than fewer defects in production — coverage can be manufactured, and a suite written to satisfy a target teaches nobody anything. And if the real problem is that nobody owns quality after handover, say so first: we would rather design the transfer than deliver a suite that decays.
Frequently asked questions
Where this service usually sits.
Tell us what you’re building.
Describe what exists today and what you need. An engineer reads it and replies in writing with how we would approach it, and the model that fits.