ntoTeq
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Your data knows
more than you think.

You just have to see it.

OntoTeq reveals the structures, relationships and patterns already present in your data.

Explore what we do See how it works
The idea behind OntoTeq

Unrevealed knowledge is an unexploited resource.

39.85%
of EU enterprises with 10+ employees performed data analytics in 2025 — in-house or via a provider. Six in ten did none at all.
Eurostat, 2025 · isoc_eb_das
“80% of industrial data is still collected and never used. This is pure waste.”
Ursula von der Leyen, President of the European Commission
State of the Union, 16 September 2020 · source

The question isn’t what data you need. It’s what your data can already tell you.

Who is this for

If you already have the data —
but not the full picture.

OntoTeq isn’t tied to one industry. Wherever data has accumulated, the same questions apply.

Sectors

  • Energy & utilities
  • Manufacturing
  • Construction & infrastructure
  • Engineering & industrial projects
  • Finance & insurance
  • Healthcare
  • Public sector

Typical situations

  • Large capital projects
  • System & data migrations
  • Access & identity governance
  • Asset & lifecycle management
  • Compliance & traceability
  • Consolidating fragmented data

In data such as

  • Access rights & entitlements
  • Engineering & asset data
  • Documents & records
  • Classifications & structures
  • Operational & transaction logs

If you recognise your organisation here, your data likely knows more than you can currently see.

Why value stays hidden

The value is often in what
connects the data.

Disconnected Without context Implicit Hard to see

Data gives us evidence. Relationships give it context. Patterns give us something to investigate.

How we think

We don’t start
with the answer.

We start by asking what else the data can tell us.

“The greatest value of a picture is when it forces us to notice what we never expected to see.”
John Tukey, Exploratory Data Analysis, 1977
structures relationships context recurring patterns inconsistencies dependencies clusters similarities traces of past decisions implicit behaviour

What comes back is usable structure — patterns, connections and information that weren’t accessible before.

How we work with your data

Not AI-driven. Your data stays yours.

OntoTeq reveals structure with transparent, established methods — graph and pattern analysis, statistics and semantic modelling. Your data is never exposed to AI models or third parties, and every result is traceable back to the data behind it.

Discovery, not guesswork

Finding the unseen in existing
data is a discipline.

Knowledge Discovery in Databases
“Identifying valid, novel, potentially useful, and ultimately understandable patterns in data.”
Fayyad, Piatetsky-Shapiro & Smyth, 1996. DOI
Proven, not theoretical
Your systems already record how the work actually happens.

Process mining reconstructs real processes from the event logs organisations already keep — applied across finance, logistics, healthcare and manufacturing.

IEEE Task Force on Process Mining · van der Aalst, 2011. DOI
How it starts

Bring one question.
And the data you already have.

1

A conversation

What are you trying to understand? What data exists, and in what state? That conversation costs nothing and commits you to nothing.

2

A bounded investigation

We work on an extract of data you already have — a register, a list, a log, an export. As it is. No integration project, no clean-up first: the inconsistencies are part of the evidence.

3

A readout

What the data turned out to contain — structures, patterns, inconsistencies, dependencies — with every finding traceable to the records behind it. And, just as clearly, what it doesn’t contain.

What happens next depends on what we found. Sometimes it’s a report you act on. Sometimes a structure you adopt. Sometimes a tool. Sometimes nothing more is needed.

Where it leads

Same capability. Different data.
Different discoveries.

Three kinds of data with nothing in common — who may access what in an organisation, how a technical project is put together, and what a folder of documents actually contains. Read the same way, each gave up something nobody had seen.

Identity & access data

Who may do what in an organisation: users, roles, permissions — and the systems they apply to.

What existed
Thousands of individual permissions, granted one at a time over years. No role model.
What we looked for
Repetition. Which permissions travelled together, and which assignments broke the pattern.
What became visible
The organisation already had a role model. It had just never been written down.
What it became
Clariam — role and rule discovery for identity & access
Grounded in role & policy mining and the NIST/ANSI access-control model.

Engineering design

How a technical project is put together: systems, components, tags, documents, requirements.

What existed
Systems, tags, documents, requirements — complete in each tool, connected in none.
What we looked for
The relationships the source systems don’t hold: what specifies what, and what a change touches.
What became visible
A project that looked like a set of files turned out to be one connected structure.
What it became
Know-Y — connected, navigable technical knowledge
Grounded in IEC 81346, systems engineering and semantic interoperability.

Document data

Thousands of files in folders: specifications, drawings, reports — named by convention, indexed by nobody.

What existed
A document package of thousands of files. Complete on disk, opaque as a whole — no index, no map.
What we looked for
What each document is — and which documents cite, specify or depend on each other.
What became visible
The package already had a structure: a web of references nobody had ever drawn.
What it became
OntoTeq Docent — the engine that maps a document collection
Grounded in DS/EN 61355, IEC 81346 and deterministic classification. No LLM.

Different problems. The same way of looking at data.

In the real world

Hidden knowledge, made visible.

The three stories above end the same way: data that was already there, now structured and connected. ContextTAG is what that looks like when it leaves the screen. A plate on a valve or a marker on a cable that says not only what you are looking at, but what it belongs to, what it does, where it sits and what feeds it. Every line comes from data you already have.

See ContextTAG Reading guide (PDF) The path to AI readiness
−K11.HG10 20kV Supply A −TAA01 Transformer 20/0.69kV LOC+BLD01.TA01SUPPLY FROM−K11.HG10.UCA01.XBC03 -PLT.A01.K11.HG10.TAA01

A ContextTAG. What existing data already knows.

The core insight

A person. A system. An access right. A component. A document.

Each tells us something. The relationships between them tell us more.

“We will never understand complex systems unless we map out and understand the networks behind them.”
Albert-László Barabási, Network Science, 2016. networksciencebook.com
Who is OntoTeq

An engineer who learned to read data.

Lasse Hvidbjerg is an electrical engineer with around 25 years across complex, multidisciplinary infrastructure and industrial projects — work where thousands of systems, objects and documents only make sense through the relationships between them.

The hardest problems slowly stopped being about individual components and became about structure: how things relate, where information belongs, what context is missing, what can be reused. That widened the view from physical systems to the information behind them — into modelling, classification (IEC 81346), data structures and, in the end, software.

Not an engineer who became a developer. Years of real-world complexity became a transferable way of thinking about data — and Clariam, far outside engineering, is the proof.

What else does your data know?

We won’t pretend to know the answer before we look. But we know how to start looking.

Talk to OntoTeq