User Story Readiness
Find ambiguous language, missing acceptance criteria, assumptions, edge cases, dependencies, and source-backed clarifying questions.
Azure DevOps-native User Story readiness, test coverage, and RBT
Clearer User Stories. Stronger tests. Evidence-backed RBT decisions.
HeyAI reviews Azure DevOps work items with project context, finds missing requirements, generates test scenarios, and recommends testing scope with source-backed evidence and human review.
10-25User Stories for a first audit
6RBT decisions supported
EveryRecommendation includes evidence
The wedge
Built for the work that decides sprint quality
HeyAI is not another generic AI chatbot. It focuses on the weekly workflow where product, QA, and delivery teams decide whether a User Story is clear enough, tested enough, and safe enough to move forward.
Test Coverage Intelligence
Generate practical test scenarios across happy path, negative, regression, integration, data, and edge-case coverage.
Risk-Based Testing
Recommend skip, manual, automation, hybrid, investigate, or blocked with rationale, confidence, and suggested action.
Release Confidence
Roll up unresolved gaps, weak coverage, defect history, risky modules, and UAT focus into a readiness signal.
Before and after
Turn a vague work item into a reviewable delivery decision
User Story looks ready, but the risk is hidden
- Acceptance criteria miss reversal and override behavior.
- QA drafts tests manually from incomplete context.
- RBT decision depends on tribal knowledge.
- Risk shows up late during QA or UAT.
Team sees the gaps before sprint commitment
- Clarifying questions are ready for refinement.
- Test scenarios are drafted from User Story and linked context.
- RBT scope is explained with evidence and confidence.
- Open risks can be posted back into Azure DevOps.
Ideal early teams
Focused on Azure DevOps teams with real QA and delivery risk
HeyAI is strongest when teams already manage User Stories, defects, test planning, and release decisions in Azure DevOps, especially in regulated or process-heavy environments where unclear requirements become expensive.
QA and test leadsReduce manual test design effort and make RBT decisions easier to defend.
Product ownersCatch vague User Stories and missing acceptance criteria before sprint planning.
Delivery managersSee requirement, coverage, and release risk before the sprint becomes expensive.
ADO admins and securityUse Entra-first access, server-side secrets, auditability, and human-approved recommendations.
ADO-native workflow
First value without waiting for a full platform rollout
Start with one User Story or one Azure DevOps project. Use the extension and deeper project context when the team is ready.
Analyze a User StoryPaste or select a work item with title, description, and acceptance criteria.
Find gapsReview missing requirements, assumptions, edge cases, and clarifying questions.
Draft testsGenerate scenarios that QA can accept, edit, or reject.
Decide RBTChoose manual, automation, hybrid, skip, investigate, or blocked with evidence.
Close the loopPost questions back to ADO, re-run after changes, and track readiness over time.
Trust model
Trusted AI for delivery decisions, not blind automation
HeyAI is designed around source artifacts, confidence, audit trails, and human review. When project context is weak, the right answer is to say what is missing rather than invent certainty.
Azure DevOps-native and Entra-first, with PAT fallback where needed
Source artifacts and confidence on recommendations
Server-side secrets and no browser-exposed AI keys
Human-reviewed recommendations and insufficient-evidence behavior
Private Azure deployment
Run HeyAI in your Azure subscription
For enterprise teams that cannot store project data in a vendor-hosted database, HeyAI can be deployed into the customer's Azure subscription. The customer owns the API, UI, PostgreSQL database, vectors, logs, connector credentials, and AI keys.
Customer-owned Azure data plane
Azure OpenAI or OpenAI key stays in customer Key Vault
No outbound HeyAI telemetry by default
Customer-pulled updates with migration scripts
Copilot and MCP
Bring HeyAI project intelligence into agent workflows
HeyAI MCP is a remote, Entra-authenticated MCP server for GitHub Copilot and other MCP clients. Users can ask Copilot to list their HeyAI projects, inspect project context, generate test scenarios, and request RBT plans while keeping access scoped to the signed-in user's HeyAI permissions.
Endpoint: https://heyai-mcp.azurewebsites.net/mcp
OAuth metadata for MCP discovery
Dedicated HeyAI MCP Entra app and MCP.Access scope
Project access checked before every project tool
Pilot offer
Azure DevOps User Story Readiness and RBT Audit
For design partners, HeyAI can review a focused set of real User Stories and return gaps, test scenarios, RBT recommendations, and a weekly risk/coverage summary.
Scope
One Azure DevOps project, one team, and 25-100 User Stories over a 30-day pilot.
Participants
Product owner, QA lead, delivery or engineering lead, plus ADO admin when extension setup is needed.
Outputs
User Story gap questions, acceptance-criteria suggestions, test scenario drafts, RBT decisions, and readiness themes.
Success signal
Teams repeatedly use HeyAI before refinement, sprint planning, QA design, or release-readiness review.
Request audit
Send us your User Story/RBT pilot context
Share how your team uses Azure DevOps today. We will follow up with a focused walkthrough or a User Story readiness audit plan.
Best fit: Azure DevOps teams with active QA, product, and delivery ownership
Typical audit: 10-25 User Stories to start, then one team pilot
Prototype remains available after submission
Ready to inspect the current prototype?
Analyze a User Story with HeyAI
See the current flows for Azure DevOps onboarding, User Story analysis, generated tests, RBT recommendations, trust signals, and project context.
Try HeyAI