AI built for QA and test automation.
Multi-framework enterprise platform with end-to-end traceability, intelligent execution analysis and governance.
The Challenge
Empty test suites slow teams down — and AI alone isn't the answer.
60–70%
of QA time spent on maintenance, not new tests
+15%
defect leakage when ad-hoc AI tools generate tests without governance
3×
faster cycle times for teams using QA-specialised AI vs. generic copilots
Traditional QA tools don't leverage AI natively
Manual scripting and disconnected automation can't keep up with shorter release cycles.
Generic AI assistants generate tests without governance
No audit trail, no project context, no integration with the QA lifecycle.
No traceability between requirements, tests and execution
Quality reporting becomes guesswork. Compliance evidence is incomplete.
Scalability bottlenecked by team size
Coverage growth is linear with headcount — until QA-specialised AI breaks the curve.
Capabilities
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
Comparison
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 | ✓ | — | ✓ |
Architecture
Flexible, secure and enterprise-ready.
Multi-model AI support and deployment options to fit every context.
Inputs
Functional requirements
User stories / Specs
Existing tests
Natural language
→
TestPilot AI Platform
RAG Engine
Project context, templates, test patterns
Multi-model LLM
GPT-4 · Claude · Llama · Mistral
Fine-Tuning
Continuous adaptation per client context
Enterprise Governance
RBAC · Audit · Standards · Traceability
CI/CD integration:
GitHubGitLabAzure DevOpsJenkins
→
Outputs
Selenium scripts
Cypress scripts
Playwright scripts
Robot Framework
Reports & insights
Deployment options
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.
Packages
Scales with your team.
From early AI adoption in QA to mission-critical enterprise operations.
Starter
Initial AI adoption in QA
- Multi-framework test generation
- Selenium, Cypress, Playwright, Robot Framework
- Requirements → automated scripts
- Dashboards and reporting
Most popular
Professional
For teams scaling QA
- Everything in Starter
- Predictive suite maintenance
- Coverage and gap analysis
- Smart execution analytics
- CI/CD integration
Enterprise
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
FAQ
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.
Get startedQA-specialised AI.
QA-specialised AI.
Let's talk.
Personalised demo, no commitment. We'll show how TestPilot AI fits your context — frameworks, infrastructure and governance.