Gradion runs a dedicated quality engineering chapter: a specialist discipline within the delivery network, separate from the development teams that write the code. That separation matters. QA engineers embedded in a squad are not just developers who also write tests. They own the test strategy, define quality gates, and carry the accountability for what ships.

The chapter covers the full testing spectrum and can be deployed on demand: a single QA engineer embedded in an existing squad, a full testing team standing up automation infrastructure from scratch, or a rapid quality audit of a codebase before a major release. Engagements are sized to the situation and can scale up or down as the delivery phase changes.

Where governance allows, Gradion applies AI-assisted test generation to compress coverage timelines: test cases generated from specifications, regression suites built from production traffic patterns, and mutation testing run at a scale that manual authoring cannot match. The output is coverage that reflects how the system is actually used, not how it was designed to be used.

What we deliver

Manual testing:
Functional testing against acceptance criteria, exploratory testing without a script to find the failure modes that requirements never anticipated, usability testing with real users, and structured acceptance testing that gives stakeholders a basis for sign-off. Human judgment applied where automation produces false confidence.

Test automation:
Unit and integration tests written alongside the code, not after it. Contract testing between services. Non-regression suites that run in CI and protect production behaviour across deployments. Tooling matched to the stack: Jest and Cypress for JavaScript frontends, pytest and Playwright for Python, JUnit and Testcontainers for JVM workloads.

AI-assisted test generation:
Specification-driven test case generation, coverage gap analysis on existing suites, and mutation testing at scale. Applied where it accelerates delivery without reducing signal quality. The test output is reviewed by QA engineers before it enters the pipeline.

Performance and load testing:
Performance budgets defined before development begins, load scenarios built from real traffic patterns, and tests run in environments close enough to production to be meaningful. Bottlenecks found in a load test are a learning. Bottlenecks found during a product launch are a crisis.

Security testing in the pipeline:
Static analysis, dependency scanning for CVEs, secrets detection, and OWASP-aligned review of authentication and authorisation logic integrated into CI. For regulated environments, testing structured against a defined threat model rather than generic scans.

Proof in production

IDNow, a leading provider of AI-powered identity verification operating in a regulated environment, scaled its Gradion-embedded team from five to fifteen engineers. Quality assurance was a dedicated function within that team from the start, covering backend, mobile, and machine learning pipelines where a defect carries regulatory consequence, not just a user complaint. The QA function was embedded into the squad structure, not run as a separate review gate.

A global credential verification platform was losing 30 percent of engineering capacity to manual release management. Gradion introduced automated deployment, infrastructure as code, and monitoring across the pipeline. Release errors dropped, engineering time was recovered, and 99 percent of deployment steps ran without manual intervention. Quality gates moved into the pipeline and stayed there.

CTA

Describe where quality is breaking down in your delivery process. We will assess the gaps and propose a testing strategy that fits your team and your release cadence.