LABS
Let's talk

QANTUM LABS / QA & AI

QA automation in CI/CD to inform every release

Continuous integration and continuous delivery need quality signals the team can interpret. We design an execution strategy that combines speed, coverage and useful failure evidence.

Talk to QAntum Labs

Which tests to run at each pipeline stage

We review the journey from a pull request to deployment. Fast checks can validate logic and contracts; integration and end-to-end suites need suitable environments and data. We agree what runs per change, on a schedule and before a significant release.

For example, a payment service change may need contract and checkout tests. Running all regression tests on every commit may delay feedback; running only unit tests may leave a critical integration unchecked.

Quality gates and a policy for flaky tests

We define which results block a release and how exceptions are reviewed. A quality gate should express an understandable risk criterion. Duration, failures, relevant coverage and AI regressions can provide different signals that need separate interpretation.

When a test is flaky, we record its behavior, assign an investigation and agree a retry or quarantine policy. A result that passes after several attempts needs follow-up; its history should retain that information for the team's decisions.

Results and evidence connected with development

We review how the pipeline preserves logs, traces, screenshots and references to the tested change. Results should let the team reconstruct failure conditions. We can work with tools such as GitHub Actions, GitLab CI, Jenkins or Azure Pipelines according to the project's infrastructure.

RunTrail brings executions and evidence together to investigate quality. Connections to Jira or Azure DevOps requirements add context about affected behavior. We agree the result publishing workflow and responsibilities so that failures reach the right team.

Questions about QA and AI.

What does integrating QA into CI/CD involve?

Defining when tests run, preparing data and environments and using results to decide whether a change can progress. It also includes preserving evidence and agreeing how failures are investigated.

Can AI evaluations be added to a pipeline?

Yes, with an evaluation set and acceptance criteria suited to the system. Versions, results and variability should be recorded, with human review for cases whose quality cannot be decided by an automated check.

QA architecture for tests you can maintain

Test architecture connects coverage, data, environments, execution and evidence. We design and review these pieces so that automation can evolve alongside your product.

QA for artificial intelligence you can evaluate

Artificial intelligence applications need quality criteria adapted to their behavior. We help define evaluations for LLMs, RAG systems and agent workflows using representative cases and reviewable evidence.

Bring this strategy to your project.

Tell us how your team works, which tools you use and which risks you need to address. We can discuss the scope of a consulting or evaluation engagement together.

Talk to QAntum Labs