Home / Data and AI / Semantic layer
Semantic layerOne meaning for all your data.
We define once what every metric, customer and product means, so dashboards, people and AI agents speak the same language.
What is a semantic layer?
A semantic layer translates the data in your ERP, CRM and databases into concepts with a single, agreed definition, such as sales, margin or customer. N.Technology designs it, governs it and connects it to your dashboards and AI tools, so every team, report and AI agent gets the same answer to the same question.
From technical data to your organization's language.
The piece that makes AI answer with your correct numbers, not with assumptions.
Your organization's metrics and entities
We define sales, margin, customer, product or case file once, along with their calculation rules.
On top of your sources, without moving them
It connects to your ERP, CRM, data lake or data warehouse, on-premises or in the cloud.
Security in a single layer
Role-based and row-level permissions applied the same way to dashboards, applications and AI agents.
Trusted context for AI
Models, the knowledge base and AI agents query certified definitions and cite the source.
Open to your tools
Dashboards, spreadsheets, applications and APIs all read the same numbers.
Operated and versioned
Controlled changes, lineage for every metric and 24/7 operations as part of our managed IT services.
What it solves
- Every department has its own version of the numbers
- AI makes up or misreads figures
- Different permissions in every tool
- Reports that take weeks to reconcile
- Switching tools means rebuilding everything
From assessment to operations.
A single accountable partner at every stage.
Assessment
We inventory sources, key metrics and where the numbers contradict each other.
Design
We model entities, metrics, hierarchies and permissions in a single layer.
Implementation
We connect your sources, dashboards and AI, and certify the first metrics with your teams.
Operations
We operate, version and expand the layer across your organization, one area at a time.
What clients ask us.
How is a semantic layer different from a data warehouse?
A data warehouse stores and organizes data. A semantic layer gives that data a shared meaning and serves it with the same rules to any tool or AI agent. Ideally, you have both: the warehouse as the foundation and the semantic layer as the common language on top of it.
Why does AI need a semantic layer?
Because a language model does not know what "margin" means in your organization. With a semantic layer, AI queries certified definitions and answers with your correct numbers while respecting each user's permissions.
Do I have to change my analytics tools?
No. The semantic layer connects to the analytics tools you already use and to the ones you adopt in the future, so your teams keep working where they are today while reading the same numbers.
Does my data leave my organization?
Not necessarily. The semantic layer can run on your own infrastructure, in colocation in Mexico, or in the cloud and region you choose, so you decide where your data lives.