The Dynizer Platform
Intelligence comes from models.Trust comes from architecture.
AI shouldn’t be a free-floating interface. It reaches full value only when built on a governed node inside your enterprise — grounded, permissioned and auditable. Dynizer is that node: the semantic foundation for governed enterprise AI.
Meaning
what each thing actually means
Context
how facts relate across systems
Provenance
where every fact came from
Control
who may see and do what
One structure for everything
Documents and databases finally speak one language.
The same fact can live in a report — or in a table. Dynizer reads both, and stores them identically.
| id | actorWHO | amount_eurWHAT | cityWHERE | quarterWHEN |
|---|---|---|---|---|
| 4708 | committee | 1 250 000 | Ghent | 2025-Q2 |
| 4709 | cfo | 3 400 000 | Berline4 | 2025-Q2 |
| 4711 | boarde1 | 50 000 000e3 | Berline4 | 2025-Q3e5 |
| 4712 | boarde1 | 900 000 | remote | 2025-Q4 |
One fact, two sources — stored once, in the same five dimensions. The overlapping values board · €50M · Berlin · Q3 2025 become shared elements — one element, one ID, wherever they appear: the Berlin in row 4709 and the board in row 4712 are the same elements again. That overlap is exactly what the Data Discovery Service uses to link documents to records — automatically, with no foreign keys and no integration project. Every fact stays traceable to its origin: the exact sentence, or the exact row.
The sentence is the unit of truth
Every sentence is stored individually, with its text, position and importance — not as an arbitrary chunk. Provenance is exact. For database records, the same trace leads to the exact row.
One element, one ID
Every distinct value is stored once. “Siemens AG” in a contract row and in a meeting note are the same element — links across sources are a property of the data model, not an integration project.
Determination, not probability
Queries return exact, countable facts — typed instances you can inspect — not passages a model guesses might be relevant.
Not a feature a competitor can bolt on — a property of the data model, protected by multiple granted patents.
Four components, one engine
From raw sources to grounded answers.
Analyzer
Reads documents, transcripts and media and decomposes every sentence into WHO/WHAT/WHERE/WHEN/ACTION facts. Fully automatic: no ontology to design, no model to train, no tagging to maintain.
Core Store
The patented semantic store. Every element stored once; relationships held as compressed bit-matrices. SQL-compatible backend on standard infrastructure. Full-text, vector and bit-index built automatically on ingest.
Data Discovery Service
Unlocks existing databases into the same semantic model — PostgreSQL, MySQL, MSSQL, Oracle — links records to documents, and turns structured records into accurate natural-language sentences.
Chat API
The interaction layer for people, applications and agents: grounded answers with citations, per-user-group access control, and precise structured querying via DQL. Works with the LLM of your choice, including locally hosted models.
Why the answers hold up
Four layers. Zero blind spots.
Every question runs through four complementary retrieval layers in parallel: vector similarity catches paraphrase; the entity filter catches exact names; the semantic filter matches on WHO, WHAT, WHERE, WHEN and ACTION; keyword search catches literal terms. Each layer’s blind spot is covered by the others — no weight-tuning required. The vector layer broadens; the core decides.
The LLM is a presentation layer, not an inference layer. It presents; it does not invent. If the answer is not in your data, the system says so — enforced by architecture, not by a prompt.
Dynizer — hallucination-free by design.
Where it fits
Everything a knowledge graph promised —without ever building one.
Knowledge graphs were right about the goal: AI needs meaning, relationships and context. Dynizer delivers them differently — semantics derived from the data itself, automatically. A self-building index: always current, zero upkeep, covering documents and databases in one structure.
A catalog knows about your data. Dynizer knows what’s in it — and hands your AI facts it can prove. Complementary by design: policy flows down; grounded facts flow up.
What analysts call it: AI-ready data · GraphRAG without the graph · RAG on your data · data-to-text · AI TRiSM · data sovereignty. What it is: one semantic core that does what those categories promise.
Built for your estate
No exotic infrastructure. No data migration.
Dynizer’s core is SQL-compatible and runs on standard infrastructure, co-located with the databases it serves — with native Oracle support across the estate. Where it captures change and lineage, it uses the database’s own audit and recovery machinery: no triggers, no code instrumentation, no schema changes. Proven at 500 million rows per day.
Your platforms stay. Dynizer is the lightweight semantic layer between where knowledge is stored and where it is used — middleware, with or without storage.
Safe to deploy
Your data. Your models. Your budget.
Runs where you decide
Cloud, private cloud, on-premise, or fully air-gapped with a local language model. No external AI service is required.
Private by design
Personal data pseudonymised at ingest, before anything reaches an external model. Access control per user group; every query logged, every answer auditable.
Predictable by design
No pay-per-token. Run the model you choose — private, local, or none at all — at a cost you can plan.
Get in touch
See it on your own data.
The fastest way to evaluate Dynizer is not a slide deck — it’s your documents and your database, live. 10 days to a working semantic dashboard.