Fragmented coverage
Manual planning and disconnected tools leave gaps across applications, data and AI systems.
Agentic quality engineering
From requirements
to verified fixes.
Autonomously.
Orchestrate intelligent AI agents across your entire quality engineering lifecycle. Analyze requirements, design tests, automate execution, diagnose failures, and prepare human-approved code fixes and pull requests.
ORCHESTRATION MAP PRODUCT VISION
Quality OrchestratorVerifyStack.ai
Seven domains · One orchestrator
Coverage / 01
One intelligent platform. Seven quality domains.
Explore how specialist agents bring software, data and AI quality into one governed lifecycle.
01
Application Quality
Exercise critical journeys across the UI, APIs and integrated services.
UI automationAPI testing
Explore domain 02
AI/ML Quality
Evaluate the data, features and model behavior that shape production predictions.
Dataset validationModel evaluation
Explore domain 03
LLM Quality
Evaluate generated responses, retrieval and agent behavior against explicit criteria.
Prompt testingResponse evaluation
Explore domain 04
Data Quality
Check that records remain accurate and traceable across data pipelines.
Database testingSchema validation
Explore domain 05
Infrastructure Quality
Check deployments, configuration and resilience across environments.
Cloud infrastructureDocker
Explore domain 06
Security Quality
Identify potential weaknesses and prepare remediation proposals within an explicitly authorized scope.
AI securityApplication security
Explore domain 07
Governance
Make review decisions and supporting evidence visible across the lifecycle.
Human approvalAudit trails
Explore domain The challenge / 02
Traditional testing is no longer enough.
Fragmented tools, manual handoffs and more complex applications make it harder to see what was tested, why it failed and what needs to change.
Slow diagnosis
Teams spend time joining logs, test results and requirements to identify root causes.
Unclear remediation
Manual review and scattered evidence make fixes harder to trace from finding to approval.
Lifecycle / 03
Follow the evidence from intake to pull request.
Step through a sample workflow and see what each agent produces and where a person must approve the next action.
LIFECYCLE EXPLORERILLUSTRATIVE DEMO
Stage 01 / 14
Requirements intake
Quality OrchestratorCapture a requirement, acceptance criteria and scope.
EXPECTED OUTPUTVersioned intake record
Human control / 04
Autonomous intelligence. Human-controlled decisions.
Execution follows the approved plan. A proposed repository change requires separate authorization, and verification runs before pull request creation.
01Review the test execution plan
02Approve the specific code change
03Verify the isolated branch
04Review the pull request and evidence
Business value / 05
Give engineering teams a shared view of quality.
Connect risk-based coverage, test results, diagnoses and remediation decisions so teams can prioritize work and trace outcomes back to requirements.
01
Plan
Requirements, risk and test design
02
Test
Approve, execute and diagnose
03
Remediate
Propose, review and verify a fix
04
Report
Open a pull request and capture evidence
See the product vision
Ready to transform your quality engineering?
Explore how a governed, agentic workflow could fit your delivery process.