Knowledge base
Multi-level tree, multi-format import, hybrid retrieval, evidence and source trails.
Platform Digital assets
Storing material is not enough — AI has to understand it
Knowledge bases, brand assets and material assets under one governance model: properly layered, properly traced, properly reviewed — so every citation draws on the right, current version.
What it solves
- Material is scattered across drives, docs and chats — AI cannot reach it and the team cannot find it.
- The same fact is phrased three different ways, and AI cites the wrong one.
- Six months of publishing later, nobody knows which material is actually cited and which is stale.
How it works
- Multi-level knowledge tree: organize entries by business line or product, import many formats, keep the hierarchy clear.
- Hybrid retrieval (RAG): vector + keyword + title channels recalled and re-ranked, injecting only what is relevant.
- Layered brand assets: brand identity, knowledge graph, brand guidelines and compliance checks kept separately but consistently.
- A governance loop: review workflow, evaluation sets, staleness detection and citation attribution — assets are maintained, not entered once.
Capabilities
Three asset groups, one governance model
Knowledge governance
Review workflow, evaluation sets to test retrieval quality, staleness flags, usage analytics.
Brand assets
Brand knowledge graph (entities and relations), brand guidelines, unified brand identity.
Brand compliance
Brand compliance checks and encyclopedia-entry monitoring — entries are how AI anchors a brand entity.
Material assets
Image libraries with AI image search, video scripts and metrics, curation boards, storage usage.
Asset operations
Asset popularity analytics, backlink ledger, build checklists and external knowledge sharing.