Post Quantum Secure | Sub 50ms O(1) Scale | Zero Plaintext
The same secure, low energy engine that drives our
trusted Enterprise solutions lets Claude search your
fully encrypted files at the Edge.
If you're building AI, RAG, or anything regulated or privacy-conscious
this is built for you.
Ultra Secure | Low Power | Deterministic | Edge Deployable
That's not a model problem. It's a memory problem. The businesses that ship reliably are the ones whose retrieval doesn't drift, can't silently return the wrong thing, and can prove — to an auditor, not just a demo — exactly what was retrieved and why.
| 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.
The Claude plugin you can run today isn't a lightweight version of DMM — it's the identical TQNN core, sized down. 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.
Sub-millisecond search across millions of encrypted records. On-premises, private cloud, or air-gapped edge — built for mission-critical, audit-bound infrastructure.
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.
One architecture. Two sizes. No lesser version — just less of it.
Every DMM search returns its own energy cost — kWh used, carbon emitted, down to the query. Most AI infrastructure asks you to take "low power" on faith. DMM shows you the number, every time.
Toridion started from a belief that data and AI systems would end up distributed, not centralised — that energy and data sovereignty aren't side issues, they're the constraint everything else has to work within. Not sovereignty as a nation-state abstraction, but as something that has to hold at the level of a single practice, a single hospital trust, a single founder's laptop.
DMM is what that belief looks like in production: low power because energy was never going to be free to ignore, deterministic because "probably right" isn't good enough for regulated data, and deployable at any scale because sovereignty that only works for enterprises isn't sovereignty at all.
Whether it's a one-founder consultancy or a thousand-seat enterprise, the same sovereign, on-premise architecture applies — because the compliance obligation is identical. DMM doesn't have a "small business" version of privacy. There's only one version.
Three connected stages. Index your data once — even the encrypted parts — then make it searchable from any LLM, API, or app your team already uses, under one auditable trail.
Point DMM at what you've already got — files, systems, encrypted stores. Hash and index once, no re-architecture.
Attach the same index to any surface your team already uses — no separate integration for every tool.
Query in real time, trace every retrieval, and hand over a ready-made audit trail — not a support ticket.
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 →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 →However you work with DMM, you're building on the same deterministic primitive — not a cut-down version of it.
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 →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 →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 →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 →We open-sourced a DMM MCP server with associative similarity search — drop DMM straight into Claude and other MCP-aware agent pipelines.
Read more →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.
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.
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)
See for yourself what blazing-fast, quantum-secure, associative memory can do for your infrastructure.
See O(1) scaling in action: Watch associative memory maintain constant-time search speeds across massive datasets without traditional vector bottlenecks.
Review security & architecture: See how the appliance deploys on-premises or at the edge, keeping data entirely within your sovereign control.
Map your integration blueprint: Discuss specific use cases for your stack, whether you are building custom developer agents or scaling MSP pipeline tools.
Toridion Ltd are providers of high performance processing and
computing technology
