AI knowledge base for project decisions and risk management
My team was building a personal-data and consent-management platform. The project had a broad stakeholder group: dozens of engineering teams, lawyers, analysts, product leaders, and technology leaders. Decisions and newly identified risks had to reach everyone involved on a regular basis.
Requirements were not fully defined up front; my team shaped them with product partners as development progressed. Responsibility for recording decisions, risks, assumptions, issues, and dependencies was distributed across the team.
I built a knowledge base managed by an AI agent. It processed approved project sources, including meeting transcripts, team discussions, and internal documentation. At the end of each day, I reviewed the decisions and risks it had identified, together with proposed assessments of impact, probability, and the mitigation options discussed.
Over time, the knowledge base became a project copilot: one agent kept the roadmap current, while another answered stakeholder questions in team chats. The system saved around 10 hours of management time each week, and the completeness of the accumulated knowledge made it possible to hand the project to another team in one week.
Back to case studies