Project Brain
Build a durable memory from Azure DevOps work items, defects, tests, comments, modules, and extracted knowledge.
Project memory. Evidence-backed AI. Azure DevOps workflow.
Project brain for software delivery teams
HeyAI turns fragmented SDLC artifacts into persistent project intelligence, helping teams improve requirements, test coverage, risk-based testing, and release confidence with source-backed recommendations.
74Release confidence
18Risk signals found
42Source artifacts cited
Claims Modernization
Project Brain
Living context graph
Work items, bugs, tests, comments, modules, and decisions are connected into one delivery memory.
Brain
PBI
Risk
Defect
Tests
Requirement gaps
3 blocking questions
Inactive member claim warning needs reversal and override criteria.
RBT recommendation
Hybrid testing
Manual eligibility review plus automated regression coverage.
Release confidence
74%
High-risk modules require UAT focus before release.
Product proof
One project brain, many delivery workflows
HeyAI is not a generic chatbot. It retrieves the relevant project context first, then reasons over the evidence.
Requirement Intelligence
Surface ambiguity, missing acceptance criteria, assumptions, edge cases, and source-backed clarifying questions.
Risk-Based Testing
Recommend skip, automation, manual, hybrid, investigate, or blocked decisions with evidence and suggested action.
Release Confidence
Bring open risks, weak coverage, failed tests, bug history, and UAT focus into one release-readiness view.
Why it matters
Move from scattered delivery data to trusted delivery intelligence
Teams already have the signal. It is buried in work items, defects, comments, test plans, pull requests, pipelines, and decisions. HeyAI organizes that signal into context teams can use before work begins, while work changes, and before releases go out.
Persistent memoryKnowledge survives sprint boundaries, team changes, and handoffs.
Evidence-backed reasoningEvery recommendation shows sources, confidence, and suggested next action.
Azure DevOps nativeDesigned to embed into work item, sprint, test, PR, and release workflows.
Enterprise controlGovernance, permissions, auditability, and clear model boundaries are first-class requirements.
Use cases
Built for the people making delivery decisions
Product Owners
Improve PBIs before development with gap questions, acceptance criteria, assumptions, and edge cases.
QA Engineers
Generate happy path, negative, regression, integration, data, and edge-case scenarios.
Developers
Understand impacted modules, historical defects, requirement intent, and test focus before implementation.
RBT Leads
Make evidence-backed testing scope decisions with confidence, rationale, and source artifacts.
Release Managers
See high-risk work items, weak coverage, unresolved gaps, and UAT focus before go/no-go decisions.
Leadership
Track quality trends, AI usefulness, repeat defect patterns, and systemic delivery risks.
How it works
Context first. Reasoning second. Sources always.
Azure DevOpsWork items, bugs, tests, comments, links, areas, iterations
Raw artifactsStored with traceability, revisions, hashes, and source URLs
Knowledge extractionBusiness rules, assumptions, edge cases, risk signals, coverage facts
Retrieval and graphRelevant context package built before every AI call
AI recommendationsStructured answers with reason, sources, confidence, and action
Trust model
HeyAI treats AI output as a recommendation, not truth
The product is designed around source artifacts, insufficient-evidence warnings, confidence, audit trails, and human decisions. If the context is weak, HeyAI should say so.
Source artifacts on every recommendation
Confidence and suggested action
Prompt and model version tracking
Permission-aware retrieval design
Ready to inspect the product shape?
Explore the HeyAI prototype
See the current flows for Azure DevOps onboarding, PBI analysis, grounded project Q&A, RBT, context graph, and AI evaluation.
View prototype