AI built for QA and test automation.
Multi-framework enterprise platform with end-to-end traceability, intelligent execution analysis and governance.
Empty test suites slow teams down — and AI alone isn't the answer.
Manual scripting and disconnected automation can't keep up with shorter release cycles.
No audit trail, no project context, no integration with the QA lifecycle.
Quality reporting becomes guesswork. Compliance evidence is incomplete.
Coverage growth is linear with headcount — until QA-specialised AI breaks the curve.
An end-to-end AI platform for the QA lifecycle.
From specification to governance. Not a generic coding assistant — AI specialised in QA.
Intelligent Test Generation
Build multi-framework suites faster with context-aware AI.
- Scenarios from requirements
- Positive, negative and edge cases
- Reusable templates and components
- Executable scripts from natural language
Requirements to Automation
Turn functional specs into executable tests across frameworks.
- Functional requirements interpretation
- Reduced technical preparation effort
- Manual to automated test conversion
Smart Suite Maintenance
Maintenance is the silent killer of QA velocity. Not anymore.
- Detection of stale or redundant tests
- Update suggestions for changed scripts
- Impact analysis after code changes
Execution Analytics
Result analysis with AI context for accelerated troubleshooting.
- Failure pattern identification
- App vs. script failure classification
- Insights to evolve the suite
Coverage & Quality Optimisation
Identify gaps and opportunities to strengthen coverage.
- Functional coverage analysis
- Recommendation of additional scenarios
- Redundancy and duplication detection
Governance & Operational Control
Control, traceability and enterprise readiness from day one.
- Requirements–tests–execution traceability
- Version control and methodological consistency
- Centralised artefact management
Why TestPilot AI?
QA-specialised AI — not a generic coding assistant, not a legacy tool.
| Capability | Traditional QA | AI Coding Assistants | TestPilot AI |
|---|---|---|---|
| QA-native specialised AI | — | Partial | ✓ |
| Accumulated project context | — | — | ✓ |
| Requirements–tests–execution traceability | — | — | ✓ |
| Failure pattern analysis | — | — | ✓ |
| Multi-framework support | Limited | ✓ | ✓ |
| Enterprise governance & audit | — | — | ✓ |
| On-premise / Azure AI Foundry | ✓ | — | ✓ |
Flexible, secure and enterprise-ready.
Multi-model AI support and deployment options to fit every context.
Cloud API
OpenAI, Azure OpenAI, Anthropic, AWS Bedrock. Quick setup, immediate scalability.
Azure AI Foundry
Managed models on Azure. Native enterprise compliance and integration.
On-Premise
Ollama + open-source models. Data never leaves the client's infrastructure.
Hybrid
API + on-premise mix. Flexibility per use case and data sensitivity.
Scales with your team.
From early AI adoption in QA to mission-critical enterprise operations.
Initial AI adoption in QA
- Multi-framework test generation
- Selenium, Cypress, Playwright, Robot Framework
- Requirements → automated scripts
- Dashboards and reporting
For teams scaling QA
- Everything in Starter
- Predictive suite maintenance
- Coverage and gap analysis
- Smart execution analytics
- CI/CD integration
Critical operations, multiple teams
- Everything in Professional
- Full governance and audit
- On-premise / Azure AI Foundry deploy
- Fine-tuning per client context
- SLA and dedicated support
Frequently asked questions.
How is TestPilot AI different from a generic coding assistant?
Generic AI assistants generate code without QA context, governance or traceability. TestPilot AI is built around the QA lifecycle: it ingests requirements, retains project context through RAG, produces tests with audit trails, and feeds execution analytics back into the suite.
Which frameworks are supported out of the box?
Selenium, Cypress, Playwright and Robot Framework are first-class. Additional frameworks can be enabled via the platform's adapter layer in the Enterprise tier.
Can the platform run fully on-premise?
Yes. The Enterprise tier supports an on-premise deployment using Ollama and open-source LLMs, ensuring no data leaves the client's infrastructure.
How does TestPilot AI handle data privacy and compliance?
All deployments are GDPR-aware. Enterprise tiers add RBAC, full audit logging, traceability between requirements, tests and executions, and the option to keep all inference local.
How long does a typical onboarding take?
Starter teams are productive within days. Professional rollouts typically take 2–4 weeks including CI/CD integration. Enterprise engagements are scoped per client.
Does TestPilot AI replace QA engineers?
No. It removes the manual scaffolding, maintenance and triage work, freeing engineers to focus on test strategy, complex scenarios and quality engineering.
QA-specialised AI.
Let's talk.
Personalised demo, no commitment. We'll show how TestPilot AI fits your context — frameworks, infrastructure and governance.