Plug your edge data into Claude
in seconds.
Now Claude can search your private files while they stay encrypted at the edge — the same deterministic memory architecture that runs hospital-scale deployments, sized for anything from a laptop to a data centre.
Two ways to retrieve context.
Only one of them is provable.
| Vector search | Toridion DMM | |
|---|---|---|
| Retrieval | Approximate — "closest match" by similarity score | Exact — the record, or nothing |
| Consistency | Same query can return different results as the index drifts | Same input always yields the same output |
| Latency at scale | Grows with dataset size — O(log N) / O(N) | Constant sub-50ms, regardless of scale — O(1) |
| Data handling | Requires embedding / data to leave for indexing | Queries encrypted data locally — zero egress |
| Best for | Exploratory, "fuzzy" semantic search | Compliance, audit trails, regulated data, mission-critical retrieval |
Vector databases are excellent at finding things that feel related. Toridion DMM is built for the moments you need to know, with certainty, exactly what was retrieved and why — without your data ever leaving your environment.
Do I need to replace my vector database?
No. DMM sits alongside your existing stack. Use your vector DB for broad, exploratory semantic search. Use DMM for the retrievals that have to be exact, auditable, and provably correct — patient records, contract clauses, transaction history, compliance evidence.
Your data doesn't have to leave your control — or cost the earth — to be searchable by AI.
DMM is one architecture, sized for the buyer in front of it. A hospital trust and a five-person practice face the same fines for the same breach — so both get the same guarantee, just at a different scale.
The Appliance
Sub-millisecond search across millions of encrypted records. On-premises, private cloud, or air-gapped edge — built for mission-critical, audit-bound infrastructure.
- Hospital and enterprise-scale corpora
- Full audit trail, blockchain-anchored
- Deployed alongside existing RAG/vector stacks
DMM on a Pi
The same deterministic, zero-decrypt memory, running on a Raspberry Pi 5 — for developers, small teams, and anyone who wants sovereign search without sovereign infrastructure spend.
- 32 encrypted queries/sec at 329MB RAM
- Same TQNN encoding as the appliance
- Plug in via MCP in minutes
One architecture. Two sizes. No lesser version — just less of it.
Privacy and low power aren't add-ons. They're what happens when you stop decrypting data to search it.
Most compliant AI infrastructure treats privacy and efficiency as a cost you pay on top — extra encryption layers, extra compute for audit trails. DMM's O(1) tokenised recall means there's no plaintext inference step to power in the first place. Sovereign AI, without the sovereign AI power bill.
Associative AI Memory
Plug DMM into any AI stack and instantly upgrade your retrieval layer. Sub-millisecond search across millions of encrypted records — your data never leaves your control.
Discover DMM →Twelve years in development
A fundamentally different approach to memory — one that doesn't slow down as data grows. TQNN is the engine underneath DMM, and the reason it performs the way it does.
Learn about TQNN →Two ways in — same architecture underneath.
However you work with DMM, you're building on the same deterministic primitive — not a cut-down version of it.
For Developers
Evaluate DMM in your own stack today. Open-source MCP server, GhostQL for SQL-style queries, and an integration guide built for architects who want to see the primitive work before committing to anything.
Get started on GitHub →For MSPs
Deploy DMM for your client base under a white-label licence. The same appliance you'd sell to an enterprise directly, packaged for a managed-services delivery model and recurring revenue.
See the MSP programme →Keep up with the latest in AI and associative memory, across Toridion.
Lindisfarne M1 Dataset
Toridion released a 1 million-record synthetic NHS dataset on Hugging Face, built for developers working on NHS and large-scale secure RAG.
Download here →More human-like memory
Context is second to none for LLM output — but more of it often degrades results. How associative memory reduces hallucination, cost and latency together.
Read more →DMM gets MCP
We open-sourced a DMM MCP server with associative similarity search — drop DMM straight into Claude and other MCP-aware agent pipelines.
Read more →Toridion is helping teams build and power AI that works.
Secure Document Discovery
A regulated professional services firm needed AI-assisted review across thousands of legal and compliance documents stored across multiple cloud CDNs — without exposing sensitive data to external inference models. Using Toridion DMM, ingestion and retrieval ran entirely within a sovereign, air-gapped pipeline, with full audit trails and no data leaving the client's controlled environment.
High-Speed Predictive Analytics
An SEC-registered custodial fund manager needed sub-millisecond order-book analysis across high-frequency trading data. TQNN's O(1) retrieval replaced conventional vector search, eliminating the latency penalty that grows with data volume — ranking millions of order-book states in real time without specialist hardware.
Healthcare System Integration
A health sector organisation running disconnected procurement systems needed a unified view without replacing legacy infrastructure. DMM was deployed as a secure integration layer — encrypted pipelines between systems, no centralised data store, full sovereignty retained by each system.
"DMM let us rapidly spin up complex SharePoint migration tooling, securely indexing large volumes of files across several client repositories. The outcome was multiple days of engineer time recovered — and an ongoing recurring revenue opportunity."P. Winkley, Senior Infrastructure Architect (MSP)
Request a technical demo
See DMM process enterprise workloads in real time — blazing-fast, quantum-secure, associative memory for your infrastructure. Built for the most privacy-demanding applications in the world.
- ✓ See O(1) scaling in action, across massive datasets without vector bottlenecks
- ✓ Review security & architecture — on-premises or edge, entirely within your control
- ✓ Map your integration blueprint, whether developer-built agents or MSP pipelines
