Designing AI knowledge systems for public institutions
What changes when an AI assistant has to answer on behalf of an institution — controlled sources, audience classification and accountability.
Practical writing from our project work — how we design AI knowledge systems, monitoring platforms and digital services for real institutional environments.
What changes when an AI assistant has to answer on behalf of an institution — controlled sources, audience classification and accountability.
Why document preparation matters more than model choice, and how we structure source content before any retrieval layer is built.
Lessons from consolidating indicators across many responsible institutions into one consistent, maintainable reporting view.
Seasonal teams, real-time guest communication and operational pressure — and what that experience transfers to institutional services.
The gap between a working demo and a system an institution can adopt: documentation, roles, integration and support.