Ground· Orden
The knowledge is already in your organization. AI can't find it. We structure it.
We structure knowledge, business rules and critical context into a series of governed layers (semantic, context, activation): reusable, portable and free of any vendor lock-in, so that people and agents work on the same thing.
Context, from the Latin con-textus: woven together.
Why it matters
AI is only as good as the context it understands.
Between data and agents there is a layer that almost no one builds: the one that says what each thing means in your business and under what rules it can be used.
Without it, every agent interprets things its own way, and AI answers confidently about something it doesn't understand.
Ground doesn't live in isolation: ambition is set in Guide, judgment is built in Think, with our programme for Knowledge Engineers, and execution is validated in Accelerate.
AI-ready data
Ground works hereIt represents the reality AI will face, including exceptions, errors and edge cases.
Agreed business definitions, entities and relationships, and rich metadata.
Where each piece of data comes from, what transformations it has gone through, when it was valid and how far to trust it.
The same customer, product or supplier recognized as one, even if it appears in several ERPs.
Who can use what, for what purpose, with what retention and under what privacy restrictions.
Completeness, accuracy, freshness and consistency, with visible metrics and thresholds per use case.
Catalogued, exposed through APIs or data products, in open and interoperable formats, with no vendor dependency.
Documents, emails and procedures, chunked, versioned and with metadata, because much of the knowledge that is useful to AI isn't in tables.
Quality and distribution drift are monitored, and it is updated when the business changes.
The decisions and actions AI takes, and their outcomes, are recorded so the data improves with use.
Tap each condition to see what it means.
Who it's for
Designed for those who look after the organization's knowledge
- Data and IT leaders who need AI to work on data with meaning.
- Operations leaders who want agents to understand real processes.
- Business owners who decide what each concept means and want to preserve expert knowledge.
What we see
Why do semantic layers fail?
Modelling everything
boil the ocean
They try to organize all knowledge before anyone needs it. Eighteen months later it arrives complete and obsolete at the same time.
Modelling “the single truth”
They chase a single universal definition for everything, when many business entities have context-dependent meanings.
Starting from scratch
They model from nothing, ignoring the glossaries, data models and industry standards the organization already has. Expensive, slow and redundant.
Designing only from the data model
A useful semantic layer doesn't come from the table inventory, but from the map of decisions, processes, questions and actions the organization needs to support.
Leaving it all to IT
IT can model, integrate, govern and automate. But it can't decide on its own what concepts mean.
Treating it as a deliverable, not a living product
It's delivered as a closed artefact. The day the business, a piece of data, a process or a model changes, the layer stops being reliable.
How we do it
A living layer, built on what you already have
We start with a real domain with concrete decisions, not with all knowledge.
We reuse the glossaries, models and standards that already exist. The business decides what each concept means; IT implements and governs it. And the layer stays alive when the business changes.
How we develop it
What we draw on to develop Ground
Tap each point to see what it involves.
Knowledge audit
Where critical knowledge lives, what is scattered and what only exists in a few people's heads.
Semantic layer and ontology
Definitions, entities and relationships that give a shared meaning, so AI and agents operate on the same thing.
Governed context
Business rules, processes, access, ownership, lineage and security. What makes the layer auditable and AI Act-compliant.
Evolutionary maintenance
The layer is updated with every change in the business, data or models, instead of becoming obsolete.
Let's talk about organizing your organization's knowledge.
You can start with a knowledge audit: you'll know where your critical knowledge lives and where to begin.