Conversational requirement analysis
Don’t Just Write User Stories. Have a Conversation With Them.
A user story can be written, reviewed, and still leave important questions unanswered.
KontexIQ perspectives
Practical project intelligence for software teams
Guidance for turning existing delivery evidence into clearer requirements, stronger tests, and better-informed AI-assisted decisions.
AI test scenario generation
Your Team Already Understands the Requirement. Why Should QA Start Over?
By the time a user story reaches QA, a lot has already happened.
Context-aware AI
AI Can Write the Code. But Does It Know Your Product?
AI can write code. It can generate functions, suggest implementations, explain errors, create tests, and help developers move faster.
Azure DevOps project context
Your Azure DevOps Project Knows More Than Your User Story Does
A user story can look complete and still leave important questions unanswered.
Azure DevOps user story
Before You Move That Azure DevOps User Story to Active, Ask These Questions
A user story can look ready for development.
Enterprise AI context
Why Enterprise AI Needs Context, Not Just More Data
AI systems are often given more data, more models, and more automation. But without project context, they still struggle with the decisions that matter most.
AI agent context
Your AI Agent Knows Software. But Does It Know Your Project?
An AI agent may understand common engineering practices, testing techniques, and delivery frameworks. That does not mean it understands your product.
Delivery readiness
How to Tell When a User Story Is Actually Ready
A user story can look complete and still leave the delivery team guessing.
Tribal knowledge in software teams
Your Team Already Knows the Answer. The Problem Is Finding Who Knows It.
Who worked on this before?
AI user story analysis
What If Every Question About a User Story Was Asked Before Development Started?
A user story can appear complete when it enters sprint planning.