Receive build
QA gets a build, ticket, or feature after development is ready.
QA maturity level
Rise the QA Team Maturity Level. Release Faster.
Using the AI-enabled strategy, your QA team spends less time on the operation phase, shifting to optimize the process to reduce release risk.
Level 1 QA process
How QA usually works before maturity improves
At level 1, QA is mostly reactive. The team receives work late, validates manually, reports defects, and repeats checks until the release is accepted.
Read requirements
Test scope is interpreted from stories, notes, and team context.
Run manual tests
Checks are executed by hand, often focused on visible behavior.
Log defects
Issues are reported back to engineering with evidence and notes.
Retest fixes
QA repeats affected checks until the team is comfortable shipping.
Level 1 QA can protect releases, but it depends heavily on individual effort. The next maturity step is making this work repeatable, documented, and easier to measure.
Reference: TMMi model.
Level 5 QA process
How QA works when quality becomes predictive
At level 5, QA is optimized and continuously improving. Inspired by TMMi optimization and MITRE AI maturity guidance, the team uses trusted data, responsible AI workflows, and quality signals to prevent defects before release risk grows.
Measure coverage
QA reviews current coverage across requirements, user journeys, risk areas, and recent product changes.
Review AI test scenarios
AI proposes scenarios from requirements and product behavior, while QA validates relevance, risk, and expected outcomes.
Review AI automation tests
AI drafts automation coverage, and QA reviews stability, maintainability, data needs, and integration with the test pipeline.
Measure quality
Quality signals are monitored across defects, reliability, test results, and customer impact.
Prevent defects
Lessons from each release feed back into requirements, design, engineering, and QA strategy.
Level 5 QA shifts the team from finding defects late to preventing them early. AI supports the process, but the operating model stays governed, measurable, and aligned with business outcomes.
References: TMMi model and MITRE AI Maturity Model.
Google AI testing pyramid
Layered validation for AI-enabled QA
Google Cloud describes agent testing as a three-tier strategy: start with automated unit checks, validate full agent journeys with integration tests, and reserve expert human review for quality, nuance, and correctness.
Reference: Google Cloud Agent Factory recap.
Human review in QA process remains essential for judgment, nuance, and accountability. AI improves efficiency with clearer coverage, smarter test scenarios, faster automation review, and stronger signals for confident release decisions.
Product overview
Built for teams that want QA to move with delivery.
Centralized QA Dashboard
Data from multiple platforms for faster release decisions.
AI test generation
Generate useful test cases faster from product requirements, tickets, and workflows.
Less manual QA effort
Help QA teams focus on high-value testing instead of repetitive checks.
Faster regression testing
Reduce the time needed to validate releases while keeping critical coverage visible.
Better release confidence
Improve quality visibility before production with clear reports and quality signals.