The substrate behind my work, in detail — a records store, agent recall, a knowledge graph, deterministic snapshot/replay, corrections that stay corrected, and an audit trail you can replay.
The foundation: an append-only store of typed events — decisions, mistakes, facts, tool calls, session history. Each record is embedded server-side and recallable by meaning or by exact filter. Ingest, query, timeline, compact, export to a portable file.
agent_remember / agent_recall / agent_context — the working memory my agents use across every project and session. Federated retrieval spans the records store and the knowledge graph, and returns a paste-ready context block with citations.
Subject–relation–object triples with temporal validity. Exact multi-hop traversal, reverse lookups ("who controls X?"), and corrections via replace-fact that keep the prior state as queryable history instead of overwriting it.
Snapshot a namespace to an immutable blob and restore it bit-for-bit. Time-travel queries run against historical state, and any past decision can be re-derived from the exact memory it was made with. This is the property the rest of my work leans on — Cairn's agent-audit prototype replays decisions byte-for-byte from Neruva-restored context.
The newest capability, live on the substrate: an immutable content-addressed context store. An agent's signed action record commits to the SHA-256 of the exact context it read; the store's address IS that hash, verified server-side on write and re-verified on read. An auditor holding only the hash fetches the exact bytes and replays the decision — a substituted context, tampered record, or fraudulent output each fails closed. Authority verification (who authorized the agent) comes from Cairn's offline verifier.
Tell the substrate a fact is wrong once, and the correction is enforced deterministically — recalled before every extraction or answer and injected as a mandatory override. Not retrained, not probabilistic. The same mistake literally cannot happen twice.
Records carry provenance — source file, page, confidence — and recall returns it verbatim. Answers cite their source instead of asserting unsourced facts, and the substrate refuses to invent what it doesn't hold. Groundedness is the design goal, not a feature flag.
GDPR / Quebec Law 25 deletion as a first-class operation: forget records by kind, tag, time window, or user — or hard-delete every fact about an entity in both directions — cleanly, atomically, with the audit trail intact.
Namespaces isolate projects, tenants, and people. Each of my projects — Cairn, SimGen, FavourBee, day-to-day operations — has its own memory that agents share across sessions, with no code path from one namespace into another.
Every operation is metered and logged. Combined with snapshots, that means an auditable answer to the question most agent systems can't answer: what did the agent remember at the moment it acted — and can you reproduce the answer it gave?
Neruva is infrastructure I built and run — proven daily on my own work, and open to pilot or licensing conversations. I'll demo any of the above live, over MCP.