The Problem
Knowledge systems today optimise for search and retrieval but fail at contextual understanding — the ability to hold structured, layered knowledge and reason about relationships across it.
Architecture
- 01
Hierarchical knowledge graph with temporal and contextual edge types
- 02
Structured ingestion pipeline for heterogeneous knowledge sources
- 03
Context-aware retrieval engine weighting relational depth alongside relevance
- 04
Human-in-the-loop curation layer for knowledge quality
Outcome
Working knowledge platform with demonstrably richer contextual retrieval than vector-search baselines. Ongoing development as part of Sentients research track.
Learnings
Knowledge representation is a design discipline, not just a data problem
The relationship between pieces of knowledge is often more valuable than the pieces themselves
Human-AI collaboration in knowledge curation produces qualitatively better systems
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