Standard LLMs are pattern-matching engines. They generate plausible text but have no real memory, no structured knowledge, and no provable reasoning.
NSAI combines a small neural model for natural language understanding with a symbolic context graph for permanent, structured memory. The neural part speaks; the graph remembers — precisely, forever, locally.
LLMs have a fixed token window — once full, early context is lost. NSAI stores every interaction as timestamped nodes and edges in a graph that grows without limit. Ask about a decision made three years ago; the answer is an exact graph traversal, not a hallucination.
The context graph lives on your own servers, encrypted with keys only you hold. No cloud API, no embeddings sent externally, no provider access. GDPR, HIPAA, and FedRAMP compliance is inherent — because data never leaves your perimeter.
NSAI outputs the exact graph path used to derive its answer — Alice → reports_to → Bob → manages → ProjectX. No black box. Every inference is auditable by regulators, managers, or your own systems.
Graph traversals run on CPU in milliseconds. The neural component is a tiny 1B-parameter model, not a 70B GPU cluster. No per-token API costs — inference is near-zero marginal cost at scale.
New knowledge is added as graph mutations — a single node or edge update. No fine-tuning, no retraining pipeline, no downtime. Correct a fact by removing one edge. It takes milliseconds.
Enterprise knowledge spans departments — HR, finance, legal, engineering — each with its own sensitivity boundary. NSAI runs as a cluster of local agents, one per domain. Each agent owns its graph, enforces its own access rules, and communicates only encrypted, minimal results.
A cross-domain query — "Which engineers worked on a project with >$1M budget and also received a bonus in 2023?" — is answered by three agents exchanging only IDs, never raw data. No single agent ever holds the full picture.
Every buyer and seller runs their own encrypted NSAI graph on their own infrastructure. The platform can never read it. Not even us.
Every past order, quality issue, and price negotiation is stored in the buyer's local graph — forever. A new account manager can query three years of history in seconds. The seller never sees the buyer's full strategy.
When an account manager leaves, the seller's graph retains every interaction with every buyer. Preferences, payment history, quality requirements — all queryable, all private, all permanent.
Purchase order → production → inspection → logistics → delivery. Each party owns their piece of the chain. A federated query assembles the full timeline without any party exposing internal data to the others.
Never lose institutional memory. Query Slack history, Jira tickets, and internal wikis from three years ago — exact results, not AI summaries.
Healthcare, finance, legal. Every AI inference produces an auditable graph path that satisfies regulators. No black-box decisions.
Edge devices with intermittent connectivity run their own local graph. Answer queries from local history when offline; sync delta updates when reconnected.
Buyers and sellers retain full history without exposing strategy to each other or the platform. The platform operator cannot read any graph.
Autonomous agents that operate over months write every observation and action to the graph. No summarisation loss, no context drift.
HR, finance, and legal answer joint queries using private set intersection — each department's data stays within its boundary.
NSAI is the intelligence layer inside Janooma — powering every buyer–seller interaction with permanent context, zero data exposure, and full auditability.
Talk to us about NSAI →