Every AI interaction teaches your organization something.
Memuron turns those lessons into governed institutional intelligence.
Memuron turns every AI interaction into governed institutional intelligence — a searchable, auditable, policy-controlled memory layer for enterprise AI.
- Verified decisions
- Tamper-evident ledger
- Auditable memory
- Chain of custody
Enterprise AI forgetswhat your organization learns.
On Monday a team makes a decision. On Tuesday someone issues a correction. Without governance, both versions survive — and your AI can surface either one as if it were true.
Memuron resolves the conflict at write time: is this a new decision, or a correction to one you already hold? The correction becomes verified institutional knowledge — with the prior version preserved in the ledger.
Every interaction becomes institutional intelligence.
Underneath the graphs and embeddings is a simple loop your organization can govern — where every decision is verified, recorded with provenance, and fed back into the next one.
- 01
Decision
A person or agent makes a call — backed by the context it was given.
- 02
Verification
The Guardian checks it against what you already know: verify, merge, link, or reject.
- 03
Approval
Corrections, approvals, and outcomes are captured as governed, typed events.
- 04
Provenance
Source, actor, and full history are written to an append-only ledger you can audit.
- 05
Organizational learning
Verified knowledge flows into the next decision — across teams, agents, and sessions.
The loop closes. Every correction compounds — future decisions start from what your organization has already verified, not from scratch.
Trusted decisions, institutional learning, and enterprise control.
Trusted decisions
Conflict resolution at write time, verified recall, and semantic traversal — so answers stand on what your organization actually knows.
Hybrid graph search
Vector, lexical, and link-description matches surface the memory plus the edge that made it relevant.
Semantic traversal
Ask a question and walk the edges that answer it — not just the text that matches it.
Context assembly
Prompt-ready blocks with citations, graph links, breadcrumbs, and a hard budget.
Institutional learning
Stable source identity, persistent corrections, and a memory graph agents can repair — knowledge that carries across sessions instead of resetting.
Agent-repairable memory graph
live · hover a nodeMemuron stores every relationship as a first-class memory_link, including self-loops and parallel edges agents can inspect and correct later.
Stable source identity
Attach custom_id, session, thread, source ID, and URL to every memory or document.
URL and folder ingest
Fetch pages, sync local folders, and keep files mapped to stable graph nodes.
Enterprise control
Tenant isolation, auditability, provenance, and scoped retention — the controls that let you adopt AI memory without losing oversight.
Multi-tenant scoping
Cryptographic tenant_id boundaries plus spaces for project, team, and user context.
••••
••••
Auditable ledger
Every create, update, and link is a typed, append-only event you can replay.
Scoped retention
Scope what each space can see, and retain or remove memory by scope.
MCP native. Connect Cursor and other agents over an authenticated HTTP stream — no key wrangling. Full SDK and API in the docs.
Five moves, one graph.
Follow a single memory as it moves through Memuron — connect, ingest, verify, recall. Then a fifth move that turns stored context into something you can actually trust.
- quickstart$ pip install memuronSDK · MCP · REST01 / CONNECT01
Plug in, in minutes.
One SDK, MCP server, or REST call. Runs anywhere your agents run — edge functions to long-lived services.
- spec.pdfthreadtool_call02 / INGEST02
Feed it any context.
Docs, chat threads, tool calls, decisions. Memuron extracts and types the content for retrieval — no schema wrangling.
- Guardian · verifiedmergelinkduplicate03 / VERIFY03
The Guardian checks it.
Before anything lands, the Guardian checks it against what you know: dedupe, merge, link, or reject. Not a blind vector dump.
- why did onboarding stall?04 / RECALL04
Query one living graph.
Memory, RAG, and profiles share a single semantic graph your agent traverses — not three systems you stitch together.
- append-only ledgeragent · claudeconfirmedhuman · reviewcorrectedsource · spec.pdflinked05 / COMPOUND05
Every recall is provable.
Source, actor, and full history land in an append-only ledger. Corrections compound — the next decision starts from what you've verified.
Steps 3 and 5 are the difference. Anyone can store and retrieve vectors. Verifying what enters and proving what came out is what makes the memory worth building on.
Turn AI decisions into institutional evidence.
Every decision leaves a governed trail. Reconstruct it from sources, corrections, and outcomes — narrowing with Retrieve, Trace, and Confirm — so risk, compliance, and leadership can prove what your systems knew and why they acted.
Which rollback decisions were authorized after recent production incidents?
Before a decision can stand as institutional evidence, it has to clear five bars. Memuron is built so every memory can.
Authenticity
Every memory carries who wrote it, from what source, in which session, and when — so you can prove it is what it claims to be.
Integrity
Nothing is silently overwritten. Creates, corrections, and links are typed append-only events — the record stays tamper-evident.
Completeness
Prior versions, linked sources, and corrections stay in the trail — the full decision history, not a cherry-picked snippet.
Reliability
The Guardian verifies before write; hybrid search and graph recall use governed, inspectable methods — not opaque vector dumps.
Strict Chain of Custody
Who wrote, who verified, what changed, and who recalled — a chronological trail you can replay from first write to last audit.
Retrieve records that mean “deploy decisions.”
Meaning-ranked retrieval surfaces institutional knowledge about shipping — even when the wording differs across teams and systems.
Memuron runs where your data has to stay.
On-prem, in your own cloud, or fully air-gapped — same API, same graph, same append-only ledger. Wherever it runs, the memory and its full audit trail stay inside your boundary.
In your data center.
Self-host the engine on bare metal or your own Kubernetes. The graph and the ledger never leave your perimeter.
In your VPC.
Bring your own cloud on AWS, GCP, or Azure — provisioned inside your account, under your keys, from day one.
Fully offline.
Run the whole stack air-gapped with zero outbound calls — for classified, regulated, or sensitivity-first work.
Available on Enterprise plans. Reviewing Memuron for a regulated or security-sensitive deployment? We'll walk your team through isolation, data residency, retention, and self-host options.
Talk to an enterprise architectMemuron is the chassis. Arthaanu is the engine.
Memuron handles the product — tenancy, ingest jobs, the Guardian. Underneath sits Arthaanu, an open-source semantic engine you can inspect, self-host, and swap — so the ledger, encoders, and retrieval math are yours to control. Portability and deployment flexibility, with no lock-in on the memory your organization depends on.
the append-only source of truth — objects, encodings, relations and traces
“v2.3 deploy was rolled back.”
0.88, 0.31,
-0.17, … ]
Your Agent
remembers what your organization already verified.
Every verified decision compounds into shared, governed memory — queryable across teams, agents, and sessions, never from scratch.
Give your organization a durable AI advantage.
Start with an enterprise architecture review — we'll map Memuron to your AI stack, governance model, and deployment requirements before you commit.