Our Quality Approach

Quality Gates From Idea to Production

The diagram shows nine quality gates in order, from requirements to production monitoring, with a feedback loop that sends findings from monitoring back to requirements.

1. Requirements and acceptance criteria. Every user story has written acceptance criteria that your product owner agrees to. We do not build what we cannot test.
2. Design and architecture review. A senior architect reviews the design for security, scalability, cost and fit with your systems before building starts.
3. Code review. A second engineer reviews every change before it merges. That includes human review of all AI-generated code.
4. Automated testing. Unit, integration and regression tests run automatically on every change in the CI/CD pipeline.
5. Security scanning. Code, dependencies and configuration are scanned for vulnerabilities and exposed secrets.
6. AI evaluation. AI features are tested against evaluation datasets for accuracy, groundedness and safe behavior.
7. User acceptance testing. Your users test the work against the acceptance criteria and confirm it meets their needs.
8. Release readiness. We confirm tests pass, documentation and runbooks are ready, rollback is planned and you have signed off.
9. Production monitoring. We track errors, performance, cost and AI output quality, and act on what we find.

Software Quality

We test from the smallest parts outward, because each level catches a different kind of problem:

• Unit testscheck individual functions and components.
• Integration testscheck that services, databases and APIs work together.
• End-to-end testsfollow real user journeys through the whole application.
• Performance testingchecks response times and behavior under expected load.
• Accessibility testingchecks that people using screen readers and keyboards can use the application.

Work is only done when it meets our definition of done. Code is reviewed and merged, tests pass, security scans are clean, documentation is updated and the product owner has accepted it. To add testing capacity or automation to your own software, see our QA and test automation services.

Security Built In

Security is part of daily engineering, not a final audit. Our engineers follow secure coding practices, such as input validation and safe handling of credentials.

• Static code analysis (SAST)scans source code for insecure patterns on every change.
• Dependency scanningflags open-source libraries with known vulnerabilities.
• Secret scanningstops passwords, keys and tokens from being committed to code.
• Least-privilege accessgives each person and service only the access they need.
• Threat modelingmaps how a sensitive system could be attacked and how we will defend it.

For regulated work, we design for requirements such as HIPAA and work within your compliance program. Learn more about our AI governance, security and compliance services.

AI Quality and Evaluation

AI systems can give different answers to the same question, so we do not rely on ordinary tests alone. We measure AI quality with evidence, before and after launch, and we show you the results.

• Evaluation datasetsWe build sets of realistic questions or inputs with expected results, reviewed by your subject-matter experts.
• Groundedness and accuracy checksFor RAG, which answers questions from your own documents, we check that answers are correct and supported by sources.
• Red-teamingWe deliberately try prompt injection, data leakage and misuse to find weaknesses before users do.
• Bias testingWhere outputs affect people, we test for unfair differences across groups and explain how models reach results.
• Human-in-the-loopPeople review or approve AI actions where errors would be costly.
• Monitoring after launchWe watch for drift and regressions, meaning quality that declines as data, prompts or models change.

Guardrails such as input filtering and output checks are covered in our LLM gateway and guardrails architecture. For machine learning models, our MLOps platform architecture explains versioning, monitoring and retraining. Our Responsible AI Policy sets the principles behind this work.

Data Quality

AI and analytics are only as reliable as the data behind them, so we treat data quality as its own discipline rather than a step inside the build:

• ValidationAutomated checks catch missing, duplicate, out-of-range or badly formatted data before it reaches a model or report.
• LineageWe record where data comes from and how it changes, so any result can be traced back to its source.
• Access controlsSensitive data is limited to approved people and services, and access is logged.

Continuous Improvement

• RetrospectivesAt the end of each iteration, the team reviews what worked and what to change.
• Post-incident reviewsAfter any production issue, we find the root cause and fix the process, not just the symptom. The focus is learning, not blame.
• Client feedbackWe ask for your feedback at demos and steering reviews, and act on it.

Frequently Asked Questions