Give every development team an always-on quality engineering capability.
Modern applications change too quickly for regression testing to remain entirely manual. AO combines AI-assisted test engineering with modern automation, application intelligence and delivery pipelines to discover important journeys, generate and maintain tests, identify regression risk and continuously validate software as it evolves.
What we're solving
- The larger and more integrated an application becomes, the harder it is to understand the impact of every change.
- A seemingly small modification may affect an API, workflow, customer journey, downstream system or data dependency somewhere else in the application estate.
- Manual regression testing becomes expensive and slow, while automated suites become outdated and coverage becomes inconsistent.
- Developers may not know which areas are most likely to be affected by a change.
- Quality teams can become a bottleneck immediately before release, so defects are discovered late in the delivery lifecycle or, worse, by customers in production.
How we deliver
AO deploys an AI-assisted quality engineering capability alongside the customer's development lifecycle. The capability can analyse application structure, user journeys, available requirements, APIs, existing test suites and application dependencies to help identify what should be tested and what may have been affected by a change. AI can assist with generation and maintenance of test cases while proven automation frameworks execute repeatable tests in controlled environments. Results are captured as engineering evidence rather than relying on an AI agent simply declaring an application working. Where appropriate, testing is integrated into CI/CD so that relevant tests run automatically against new builds and releases. The capability can progressively develop a map of application journeys, services and dependencies to improve regression selection and impact analysis over time. Human engineering governance remains important for risk-based test strategy, acceptance criteria and final release decisions.
What you receive
Functional testing
Regression testing
End-to-end journey testing
API testing
Integration testing
Component testing
Cross-browser testing
Responsive behaviour testing
Visual regression testing
Data-validation testing
Contract testing
Workflow testing
Accessibility testing
Selected performance and load testing
Selected security-test automation
Test-data generation
Synthetic-user journeys
Requirement-to-test mapping
Change-impact analysis
Dependency-aware regression selection
Automated test-case generation
Test maintenance assistance
Failure clustering and analysis
Defect reproduction assistance
Evidence capture, screenshots and execution traces
Continuous test reporting
CI/CD quality gates
Business outcomes
- 01Increase regression coverage.
- 02Shorten feedback cycles for developers.
- 03Identify issues earlier in the development lifecycle.
- 04Reduce repetitive manual testing.
- 05Reduce the cost of maintaining large regression suites.
- 06Improve understanding of change impact.
- 07Increase confidence in frequent releases.
- 08Create repeatable quality evidence.
- 09Free quality engineers to focus on complex scenarios and risk rather than repetitive execution.
- 10Improve traceability between requirements, changes, tests and defects.
Stack & partners
From test failure to system understanding.
Where the application supports appropriate telemetry, AO can correlate failed journeys with application traces, logs and metrics to help engineering teams identify which services or dependencies were involved. OpenTelemetry is one example of a vendor-neutral observability standard, but it is not a requirement for every engagement. The outcome is to move from the test failed towards understanding which journey failed, what changed and which parts of the system were involved.
- Correlate failing journeys with traces, logs and metrics where available
- Cluster related failures instead of reporting them individually
- Point engineering teams at the services and dependencies involved
- Capture execution evidence for triage and defect reproduction
Frequently asked
Does AI replace our QA team?
No. This is AI-assisted continuous quality engineering. It removes repetitive execution and maintenance work so quality engineers can concentrate on risk-based strategy, complex scenarios and release decisions.
Can AO work with our existing development team?
Yes. The capability is designed to sit alongside your engineering team and existing ways of working rather than replacing them.
Can existing test automation be reused?
Usually. Existing suites are assessed, and valuable coverage is retained, stabilised and extended rather than discarded.
Can testing be incorporated into CI/CD?
Yes. Relevant tests can run automatically against new builds and releases, with results published as quality gates and evidence in your pipeline.
Can AO test APIs as well as user interfaces?
Yes. API, integration, contract and component testing are supported alongside end-to-end user-journey testing.
Can the platform identify regression risk after a code change?
It can help. Dependency and journey mapping is used to highlight areas likely to be affected by a change and to prioritise regression selection. It is decision support for a risk-based test strategy, not a guarantee.
Can it test across browsers and devices?
Yes, within agreed scope. Cross-browser and responsive behaviour testing are supported, with the specific matrix agreed for each engagement.
Does automated testing guarantee bug-free software?
No. No testing approach can discover every possible defect. The objective is broader, faster and more consistent coverage, earlier detection and repeatable evidence to support release decisions.