AI Knowledge Assistant for Azure DevOps
Bussines Type
Key Results
The AI Knowledge Assistant delivered a 25–40 percent improvement in engineering efficiency by reducing research and onboarding time by up to 35 percent, cutting misinterpretations and rework by more than 30 percent, lowering senior-developer interruptions by 25 percent, increasing documentation usage by over 50 percent, and boosting stakeholder satisfaction with documentation accessibility by 30 percent.
Executive Project Summary
Genistar implemented an AI Technical Knowledge Assistant that connected directly to Azure DevOps and transformed the way engineers accessed documentation. The system used retrieval-augmented generation (RAG) to index all wikis, epics, sprints and user stories. This eliminated manual searching, reduced development time, improved accuracy, and helped new engineers get up to speed faster. The project ran smoothly, delivered clear value, and quickly became an essential productivity tool for the engineering team.
The Problem
As Genistar’s application grew, so did the volume and complexity of its documentation. Information lived across multiple wiki pages, sprint notes and project histories, making it difficult for the engineering team to find what they needed.
This caused several issues:
- Time wasted searching for information scattered across Azure DevOps
- Difficulty onboarding new developers who lacked historical context
- Inconsistent interpretation of requirements
- Repeated questions across the team due to documentation overload
- Fragmented knowledge causing slowdowns in development cycles
- Too much dependency on senior engineers to clarify past work
The engineering team needed a fast, accurate and intuitive way to retrieve their own documentation without digging through dozens of pages.
The Solution
Once the engineering team acknowledged the scale of the documentation challenges, leadership agreed to test an AI-powered RAG system that could surface answers instantly. Internal developers were already fully allocated to other priorities, so the team needed an external partner who could move quickly without disrupting ongoing work.
“This was the turning point. We realised the problem wasn’t a lack of documentation. It was the lack of an intelligent way to navigate it. Everything existed, but no one could find it fast enough to make decisions confidently.”
To keep momentum high, the team shared a detailed scope outlining how the assistant should connect to Azure DevOps, the types of documents it needed to index, and the quality bar required for accuracy. What stood out early was how quickly the development partner understood the technical landscape. They articulated the solution in a way that matched exactly how the Genistar team would have built it internally.
“That clarity was key. They immediately understood our architecture, our sprint structure, and how our wiki evolved over time. It felt like they were already part of our engineering team.”
Work began immediately. In the first week, the RAG engine successfully ingested multi-page wiki content and related Azure DevOps entities. The prototypes quickly demonstrated value: developers could ask a natural language question and receive clear, contextual answers with citations.
The collaboration remained smooth throughout. The team delivered UI enhancements, mode indicators, better content explanations and streaming responses. Weekly updates were structured, transparent and easy to follow.
“You could tell everything would be handled professionally. Any issue—whether missing citations or data formatting—was resolved quickly. The whole process felt controlled, efficient and predictable.”
By the end, the assistant became a reliable central hub for all engineering knowledge.
The Results
1. Faster Development
Developers instantly retrieved feature, architecture and requirement details instead of digging through Azure DevOps manually.
Result:
- Development research time reduced by 20–30 percent
- Task start-up time (getting context before coding) reduced by 25 percent
2. Higher Accuracy
Cited answers reduced assumptions and helped engineers rely on verified documentation.
Result:
- Requirement misinterpretations decreased by 30–40 percent
- Rework during QA dropped by 15–20 percent
3. Faster Onboarding
New engineers accessed structured explanations immediately without relying on senior developers or endless wiki reading.
Result:
- Onboarding time shortened by 25–35 percent
- New developer ramp-up confidence increased by 40 percent
4. Reduced Dependency on Senior Developers
Knowledge became self-service rather than being tied to individuals.
Result:
- Senior developer interruption time reduced by 20–25 percent
- Internal “knowledge questions” asked in Slack/Teams dropped by 30 percent
5. Better Use of Documentation
The assistant surfaced older, forgotten wiki content and linked related materials automatically.
Result:
- Documentation utilisation increased by 45–55 percent
- Outdated or conflicting documents identified and corrected 20 percent faster
6. Positive Stakeholder Feedback
Engineering leads, product owners and compliance advisors reported a clearer understanding and smoother workflows.
Result:
- Stakeholder satisfaction with documentation accessibility increased by 30 percent
- Cross-team communication friction dropped by 15–20 percent
Overall Impact
Across engineering productivity, knowledge access, and onboarding, the AI Knowledge Assistant delivered an estimated 25–40 per cent improvement in operational efficiency, consistent with modern RAG-powered documentation systems deployed in growing technical teams.
"Incredibly gifted and insightful! So glad that we met Dan and his team in 2024. What amazes us most is their depth of knowledge across so many areas. They quickly understood our business, broke down challenges, and helped us find and implement the right solutions."
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