Development speed without test speed is loss of control
AI accelerated software development everywhere. The same engineer who used to ship one feature a week now refactors three modules and prototypes a fourth. If the test process did not compress in step, more changes flow through the same narrow gate, the gate becomes the bottleneck, or worse, gets skipped - and production quietly turns into the test environment.
Hiring more testers will not catch up. What does: a regression suite that runs unattended at every step toward release, built and maintained by senior engineers who can use AI to compress what used to be a week of test scripting into an afternoon. That is what this service delivers.
Want the longer argument first? Read Tested isn’t tested.
When this fits
- Engineers ship faster (with or without AI assistance) and verification has become the bottleneck.
- “Tested” today means a Jira ticket marked passed, not a green CI pipeline that ran the full regression suite.
- Releases slip because manual test passes do not fit into the cadence the business now expects.
- The product runs in multiple locales and bugs surface only in the language someone on the team does not read.
- A modernisation, a migration or a major refactor is on the table and the existing test net would not catch the regressions.
- Audit, regulator or certification work needs documented, repeatable test evidence - not a screenshot folder.
What we deliver
Test strategy and audit
We start by auditing what you have today: where the gaps actually are, and what brings the highest ROI in the next quarter. The strategy that follows is ISTQB-anchored - a test pyramid, risk-based prioritisation, and an explicit map of what runs where (PR check, integration build, staging promotion, pre-prod gate). Tooling (Playwright, Cypress, Jest, JUnit, k6, Lighthouse, custom harnesses) is chosen from your stack, not from a vendor pitch. What you walk away with is a roadmap to a test net that compounds - measured in cycle-time-to-verified-shippable, not in tester-hours.
Automation that compounds
End-to-end suites are built with AI-aided capture: a senior engineer walks a feature once with an LLM-assisted exploration tool, and the walkthrough lands in the regression suite the same afternoon. Underneath those, unit and integration tests match the actual architecture - not a thin layer of brittle UI tests pretending to be coverage. Everything runs in CI: each pull request through the pipeline, each staging promotion through the gate, nothing forgotten by hand. A flake budget - quarantine on first flake, fix-or-delete in the sprint, kill switch when a class of test stops being trustworthy - keeps the suite trusted instead of muted. Test data is managed with privacy in mind: no real customer data in test environments.
API and cross-system verification
UI tests are one layer. Most of the bugs that escape into production live in the seams between systems - the portal showed it, but the backend integration never received it; the API returned 200 but the downstream record is wrong; the staging contract works, prod’s does not.
Our API test suites target every environment - dev, staging, pre-prod, read-only prod - on the same code path, so contract drift between stages surfaces as a test failure, not a customer ticket. On top of that, cross-system reconciliation walks a real business flow through the portal, the API and the backend integrated system, and asserts the state matches in every place: same record, same fields, same totals, same audit trail. Where it pays off, we add consumer-driven contract tests so a third-party API change does not silently break the integration two sprints later. The same flows run as synthetic monitoring in production, so an integration breakage surfaces in minutes from a canary, not in weeks from a customer ticket. Test data and fixtures are environment-aware - they survive integration partners’ rate limits, data resets and isolation rules.
Performance, accessibility and resilience
- Load and stress tests with defined SLOs and regression detection on every release.
- Accessibility checks (WCAG 2.2 AA) wired into CI - not a one-off audit before launch.
- Lighthouse and smoke tests on every deploy.
- Chaos and failure-mode drills for systems where uptime is the contract.
Security checks in the build pipeline
The code that goes through the build pipeline carries its own security gates. We treat SAST and DAST as first-class quality checks on delivered software - not a separate workstream that runs once a year.
SAST runs on every pull request: secrets, injection sinks, unsafe deserialisation and cryptographic misuse caught before the code lands. DAST runs on every staging promotion, probing the live application against OWASP Top 10 categories on real endpoints. Dependency scanning happens at build time, so a new vulnerable transitive dependency fails or warns the build by policy, not three months later by accident. Findings get triaged like any other test failure: classified, owned, time-boxed, never silently muted. Everything that lives after the artifact is built - SBOM tracking, infrastructure CVE matching, exploitability assessment - hands off to Run & Harden.
Test in the customer’s language
- Locale and i18n tests that fail loudly when CHF 1'000.50 renders as 1.000,50 or “Mwst.” becomes “MwSt.”.
- Date, currency and address-format checks for every locale you ship.
- Trilingual review of the rendered UI - by people who actually read German, French and English.
How AI shows up in our test loop
We use AI to compress weeks into days, not to ship anything we wouldn’t ship without it.
AI-aided test capture is the biggest single change: a feature gets walked once, the deterministic test joins the suite the same afternoon. When something fails, the on-call engineer asks the suite “why did this fail” and gets a ranked hypothesis from an LLM, not a wall of stack traces to read top to bottom. Generated test data and edge cases cover combinatorial spaces no human would write by hand. Every AI-assisted test asset goes through a senior review gate - the engineer who lands it owns it, same as without AI. And no customer data in public LLMs is our default: no PII fed into AI tools, and we offer self-hosted models in Switzerland or Europe when the work calls for it.
How we work
We anchor on the ISTQB body of knowledge: test strategy, management, automation, performance. So when your auditor reviews the test work, it holds up. Each engagement starts with explicit scoping, has agreed checkpoints, and ends with a written report: management summary, what runs where, the metrics that matter (cycle-time-to-verified, escape rate, flake rate), and the next sprint’s priorities.
For application security testing, including OWASP-aligned pentests and red-team work, see Run & Harden.
Why Luzid
- Senior engineers who can read your stack, your customers’ language and the test design - not a remote tester pool billed by the hour.
- Strategy, design, automation and performance - one team. ISTQB-anchored, at a quality your auditor expects to see.
- AI as a lever on the test side, not just on the dev side - so verification compresses at the same rate development does.
- Test reports that speak the language of stakeholders, not just developers.
- Trilingual in German, French and English.
Do you replace our internal QA team?
No. We work with the team you have - bringing in test strategy, automation muscle and AI-aided capture where they speed things up. The endgame is your team owning a suite they trust, not us being indispensable.
Can you take over an existing Cypress, Playwright or Selenium suite?
Yes. We start with an audit (what runs, what is muted, what is flaky, what coverage is real), then bring it under a single CI gate with an explicit flake budget. Most of the value comes from the strategy layer, not from rewriting working tests.
Do you only test the UI, or also APIs and integrated systems?
Both, and we typically lean on the API and cross-system layer because that is where most production-impacting bugs hide. We hit APIs across every environment on the same code path, and we run reconciliation flows that walk a real business case through the portal, the API and the backend integrated system - asserting that the same record, with the same fields and the same audit trail, shows up in every place. UI E2E sits on top, not as the only layer.
What does 'AI-aided test capture' actually mean?
A senior engineer walks through a feature with an LLM-assisted exploration tool. The walkthrough is captured into a deterministic, repeatable end-to-end test - reviewed and committed the same day. What used to be a week of test-script writing is an afternoon. The asset then runs unattended on every promotion stage.
How do you keep the suite from going flaky?
Every project gets an explicit flake policy: quarantine on first flake, fix-or-delete inside the sprint, kill switch when a class of test stops being trustworthy. A muted test is worse than no test.
What does it cost to set up versus to run?
Setup is a one-off block (audit + strategy + initial automation backbone), typically weeks not months. After that, every development iteration carries two ongoing test costs: updating the test suites to cover what was added or changed, and running the suites plus evaluating the results - including the triage that tells you, defect by defect, whether the right next step is a code fix, a configuration fix, or an operational workaround that holds the line until a proper fix can ship. Steady-state lands at 30% to 50% of feature-engineering capacity on most products. We aim to compress that share, not preserve it.
Where do SAST and DAST sit in your test pipeline?
SAST runs on every pull request - it reads the code statically and flags secrets, injection sinks, unsafe deserialisation and crypto misuse before the change lands. DAST runs on every staging promotion - it probes the running application against OWASP Top 10 categories on real endpoints. Both are gated by policy and triaged like any other test failure, with classified ownership and a time box. They are quality checks on the delivered software, not a once-a-year audit.
Do you also do pentests and red-team engagements?
Yes - those live under Run & Harden, our operations and security service, alongside SBOM-driven CVE response and infrastructure introspection. We treat scenario-driven security work and the production-side security posture as a separate discipline so the QA service stays focused on functional, performance, locale and in-pipeline security verification.