One Library. Infinite lenses.
SUMA stores facts once. Different personas see different truths from the same memory graph — without copying, without re-indexing, without leakage. This is POSM (Persona Octant Semantic Mapping). It is why one brain serves eight surfaces.
$ ask "What is Maya?"
# Library holds 1M Lego Blocks (one paragraph = one book).
# Then you pick a Lens…
[Family Lens] → "Maya is your team lead — joined 2024, based in Bengaluru."
[Developer Lens]→ "Maya is the engineer who fixed the auth bug last sprint."
[Philosophy Lens] → "Maya — the Sanskrit concept of cosmic illusion."
# Same Library. Same question.
# Different Lens = different mathematical truth.
# Zero leakage. Semantic RBAC at the math layer.The Library
One canonical graph. Every fact stored exactly once. Nodes carry K-WIL gravity (Recency × Emotion × Density × Semantic). No persona-specific copies.
The Lens
Each persona is a weighted projection over the same graph. Same nodes, different gravity assignments — Family weighs relational facts heavier; Developer weighs technical facts heavier.
The Math Layer
Edge groups enforce binary visibility. RBAC happens in the gravity formula itself — not in code that can be bypassed. Zero leakage by construction.
Why this is the moat
Traditional RAG copies data per persona — ten roles means ten databases, ten sync nightmares, ten audit trails. POSM solves it once: the graph stays canonical, the lens does the discrimination.
For a developer, "Maya" surfaces commit history. For their manager, "Maya" surfaces sprint velocity. For HR, "Maya" surfaces the joining date. Same fact. Different mathematical truth. Zero leakage at the math layer.
Read the patent → see the engine → deploy the brain.