Frame
Identify the knowledge and workflows worth connecting
RHUDE builds AI systems around material that already has value: documents, archives, research, media, calls, collections and operational knowledge. The goal is not to add a chatbot everywhere. It is to make information easier to retrieve, trace and reuse while automating the repetitive parts that can be automated safely.
Generic AI becomes unreliable when it lacks context, sources and clear boundaries. Useful organisational AI needs controlled ingestion, retrieval, permissions, traceability and an explicit path from answer to source.
Identify the knowledge and workflows worth connecting
Design ingestion, metadata, permissions and source traceability
Build retrieval and interfaces around real user questions
Add automation only where review, logging and ownership are clear
WebXR, AR, spatial data and provenance-aware immersive archaeology with University of Oulu.
Coming soonTechnical systems design for a multimedia hub, podcast studio and digital reading environment.
Coming soonRAG combines a language model with retrieval from your own approved sources so answers can use organisation-specific information and point back to the material they came from.
Yes. Media can be transcribed, segmented, indexed and enriched so relevant moments can be retrieved alongside documents and structured data.
Only where autonomy is justified. For higher-risk actions RHUDE favours approvals, logging and explicit business rules rather than uncontrolled automation.
RHUDE is most useful early, when creative, technical and operational decisions still have room to align.
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