Services
The QA your product needs, not a standard testing package.
Every team arrives from a different starting point. Sometimes the problem is the process; sometimes it is a manual regression, an automation suite that no longer scales, or an AI feature that is hard to evaluate. We come in exactly where it is needed and leave something the team can keep using once the project ends.
You do not need to know in advance which framework, tool or kind of testing is missing. Pick the situation closest to yours.
Book a 30-min call“We are not sure what is failing or what we should prioritise”QA Audit & Strategy
Before spending more on testing, we find out where the risk actually is.
A QA audit exists to understand the real state of quality before deciding what to change. We look at how a feature reaches the team, how it is validated, where coverage is documented, what signals exist before a release, and which part of the process is creating the most risk or cost. What comes out is a concrete, prioritised strategy, not a generic list of good practices.
- QA grew organically and there is no shared strategy.
- Test cases are spread across Excel, Confluence, tickets or people’s heads.
- There is automation, but it is unclear whether it covers the risks that matter.
- Releases depend on long manual regressions or on specific people.
- Bugs keep coming back in production and the coverage gaps are unclear.
- The team is growing and you want to order QA before adding more tools.
- Review of the whole QA flow inside the SDLC and how much shift-left is real.
- Analysis of test management, traceability, entry and exit criteria, and ownership.
- Review of functional, E2E, API/integration coverage and relevant non-functional risks.
- Framework review: architecture, stability, maintainability, data and feedback speed.
- Analysis of CI/CD, reporting, environments and test data management.
- Gaps in performance, accessibility, contracts or AI quality where they apply.
- Tooling recommended only when it solves a concrete problem.
- Improvements prioritised by risk, impact and effort.
What you get
- A clear diagnosis of the current state.
- A map of risks and priority gaps.
- A QA strategy fitted to the product and the team.
- An actionable roadmap with priorities and next steps.
- Recommendations on process, coverage, automation and tooling.
How it works. It can be bought as a standalone assessment, or used as the starting point for implementing the improvements afterwards.
Related case · German startup“We know what to improve and need it built or automated”Quality Engineering & Automation
We build automation to make feedback faster, not to pile up tests.
We can build an automation base from scratch, extend an existing suite, or rescue a framework that has become slow, brittle or expensive to maintain. The focus is protecting the flows that actually matter and making tests part of daily delivery: controllable data, reproducible runs, CI/CD, reporting and an architecture the team understands.
- Manual regression is holding releases back or eating too many hours.
- The existing suite has flaky tests, duplication or static data.
- Tests only run locally, or someone has to remember to launch them.
- Coverage sits in E2E and faster signals in API or contracts are missing.
- You want automation that is not a black box only one person can maintain.
- You want QA reliably connected to pull requests, pipelines and releases.
- Design or refactor of automation frameworks.
- Automation of critical E2E journeys and functional regression.
- API, integration and contract testing where they give a faster signal.
- Maintainable architecture: Page Object Model, fixtures, helpers and clear responsibilities.
- Test data created and torn down through APIs to cut static dependencies.
- Smoke, sanity and regression suites by risk and how often they run.
- CI/CD integration: on push, on pull request, scheduled or as a release gate.
- Reporting, evidence and failure diagnosis.
What you get
- A suite prioritised by risk rather than by volume.
- Automation built into the real development flow.
- Faster, repeatable feedback before a release.
- Less reliance on manual data or fragile environments.
- Documentation and structure the team can maintain.
How it works. The scope can focus on one concrete need — a critical regression, say — or cover framework, test data, CI/CD and reporting end to end.
Related case · Planning SaaS“Our product uses AI, or we want AI to speed up QA under control”AI Quality & AI-assisted QA
We speed up QA work with AI, and build criteria to measure the quality of the products that use it.
We treat AI as two separate problems. On one side it can cut repetitive QA work when it comes with controls and human review. On the other, a feature built on an LLM, RAG or agents needs a different evaluation strategy from a deterministic flow. We design both layers so AI adds speed without losing traceability or judgement.
- Too much time goes into turning requirements into scenarios and documentation.
- You want AI inside the QA flow without relying on loose prompts.
- Your product has an LLM, RAG, copilot or agent and you cannot tell if it improves.
- What counts as a correct answer is probabilistic and ordinary testing is not enough.
- You need to catch regressions between prompts, models, datasets or versions.
- You want each change compared against a baseline rather than by eye.
- AI-assisted flows for requirements, scenarios, documentation and test management.
- Integration with the team’s tools — Jira or Xray — to avoid disconnected processes.
- Faster automation and refactors, keeping human review and ownership.
- Representative datasets for evaluating AI-based features.
- Evaluation criteria: consistency, factuality, relevance, format and business rules.
- Regression across prompts, models and versions.
- Evaluation of RAG flows: retrieval, the context used and answer quality.
- Agent testing: decisions, tool calls, limits and adversarial scenarios.
What you get
- An AI-assisted QA flow where it makes sense, with human review.
- Explicit quality criteria for AI features.
- Reusable evaluation datasets and scenarios.
- Evaluation suites that compare across versions.
- An objective way to tell whether a change really improves the product.
How it works. It can be bought to improve QA internally, to validate one AI feature, or as part of a wider quality engineering strategy.
“We need senior QA ownership embedded in the team for a while”Embedded QA & QA Leadership
Senior QA ownership embedded with your team, not consulting from a distance.
Sometimes the problem is not knowing what to do, but having someone to lead it and do it alongside the team. We join the day-to-day of product and engineering for a while to order quality priorities, support the team and turn strategy into decisions and real work.
- There is no senior QA figure with ownership of quality across the team.
- People are testing, but direction, priorities or a shared way of working are missing.
- You need senior support through a growth stage or a complex release.
- After an audit you know what to change, but there is no capacity to do it.
- You want mentoring and handover, not permanent reliance on a supplier.
- You need someone who can talk about quality with product and with the business.
- Ownership of QA strategy and priorities.
- Taking part in refinement and defining quality criteria early.
- Design or improvement of workflows, ready and done criteria, and release criteria.
- Manual and automated coverage prioritised by risk.
- Coordination of regressions, releases and risk follow-up.
- Mentoring, pairing and reviews with other QA or development profiles.
- Clear reporting to product, engineering and leadership.
- Gradual handover so ownership ends up inside the team.
What you get
- Senior QA direction without filling a permanent role right away.
- A working process the team understands and can sustain.
- Quality priorities tied to product and business risk.
- Practical improvement, not only recommendations.
- Knowledge passed on to the internal team.
How it works. By project, part-time or as interim cover, depending on the context. The goal is to solve a concrete need and leave capacity behind inside the team.
Related case · MedTech clientThey are not four closed packages.
An audit can end in a roadmap your own team implements, or we can stay to build the automation, wire up CI/CD, validate an AI feature or take QA leadership for a while. The scope is defined around the problem and the outcome, not around a fixed list of deliverables.
Capabilities
Testing capabilities
Every product needs a different mix of techniques. During the assessment we work out which kinds of testing give real signal and which ones belong in the delivery pipeline.
Functional & Exploratory Testing
Functional validation, critical scenarios, edge cases and risk-guided exploration.
E2E & Regression Testing
Maintainable suites that protect the critical journeys and bring the cost of regression down.
API & Integration Testing
Validation of APIs, integrations, contracts, errors, data and flows between services.
Contract Testing
Verification of contracts between consumers and providers, to catch incompatibilities before integrating.
Performance & Load Testing
Smoke, load, stress and spike testing, and bottleneck analysis when performance risk calls for it.
Accessibility Testing
Automated checks and functional accessibility review as part of the delivery cycle.
Visual Testing
Detection of visual regressions in interfaces when UI consistency matters.
AI / LLM Testing
Evals of outputs, consistency, factuality, RAG, agents, prompts and behaviour regression.
Not sure which of these fits your case?
You do not need to arrive with the solution decided. Tell us what is happening today — slow regressions, automation that will not scale, no process, or an AI feature that is hard to evaluate — and we will work out the next step with the most impact.
Book a 30-min call30 minutes · No commitment · We talk about your context, not a standard package.
Want to see what this looks like on real projects? See case studies