Thinking about AI memory, search, and sovereignty.
Technical depth and strategic thinking on the institutional memory layer — for builders and enterprise teams alike.
From the blog
What is institutional AI memory?
Institutional AI memory is the structured, persistent, AI-queryable representation of an organisation's accumulated knowledge.
TechnicalAgentic search vs. RAG
Retrieval-Augmented Generation was a step forward. Agentic search — which reasons, re-queries, and refines until confident — is the next one.
ArchitectureEntity Coherence
When your CRM, email, and Slack all refer to the same person differently, your AI hallucinates. Entity Coherence resolves this at ingest.
Articles
Long-form comparisons and technical breakdowns on agentic search, GraphRAG memory, and the architecture enterprise AI actually needs.
Best Clay alternatives: why static spreadsheets fail modern sales teams
Waterfall enrichment burns credits on lists that decay in weeks. Here is the architecture that turns disposable scrapers into a permanent, self-cleaning sales memory core.
Read articleBest Apollo.io alternatives: why static B2B databases fail revenue ops
Multi-tenant contact silos decay by design — outdated titles, dead inboxes, climbing bounce rates. Live AGV verification and dynamic waterfalls replace the static repository.
Read articleNotion AI alternatives: why static wikis fail AI agents
Passive document silos become data graveyards — contradictory notes, cell-coordinate hallucinations, and runaway context costs. A living knowledge graph fixes all three.
Read articleThe Ultimate Agent OS: Claude Code, Ruflo swarms, and Chordian.ai
HNSW vector memory is blazing fast for code similarity — and useless for deterministic facts. How to bridge a verified GraphRAG fact graph into a 100-agent swarm via MCP.
Read article