Solutions/Semantic Layer for AI
Don't let your AI initiative outrun your governance.
An AI agent pointed straight at your warehouse doesn't know that "revenue" excludes refunds, or which columns it was never supposed to see. It will still answer, fluently and plausibly, on a guess. dScribe gives it the same governed definitions your BI reports already use, so it answers in your business's terms.
Not a chatbot's best guess. The same governed definition your BI reports already trust.
- Your AI agent will confidently answer with the wrong number. A fluent answer built on a stale metric or the wrong join, delivered with total conviction.
- Your AI can see data it was never authorized to see. No permission model, no ownership check, just raw table access.
- Your AI rollout multiplies whatever wasn't governed yet. One ungoverned metric answered wrong for one person is a mistake; the same metric wrong for every employee asking the chatbot is a pattern.
Context readiness
See what is ready before an assistant depends on it.
Coverage shows which fields and assets are connected to governed definitions and which still need context. It is a readiness view for AI and discovery, not universal number-level quality scoring.
How it works
A plain question, answered with the governed calculation.
Ask in your own words and get back the governed definition and its exact formula, spelled out to the line. Not a generic guess pulled from raw tables.
How dScribe helps
Ground the AI you already have.
Your AI answers in your business's terms, not a model's best guess.
The same harmonized definitions your teams already rely on in BI reports are exposed to AI systems as a governed semantic layer.
Every team's AI gets the same governed answer, not a new proof of concept each time.
Built to back chatbots and agents across the whole organization, not a single team's pilot.
Your AI can't surface what a person isn't allowed to see.
Access follows the same ownership and permissions already defined in your catalog, so it answers with what someone's actually allowed to see, and nothing else.
Live in weeks, not stalled behind a security review.
As with the rest of dScribe, this layers on top of your existing systems. No direct access to raw source data required, so security teams stay comfortable with the AI rollout.
Knowledge graph
Grounded in a graph, not a text match.
dScribe stores your business context as a knowledge graph. Every definition is connected to the columns, reports, owners and policies behind it, so an assistant retrieves the approved meaning and everything that gives it context, in one step.
- Definitions linked to the exact columns, reports and datasets they govern
- An assistant retrieves the approved meaning plus its relationships, not the closest-sounding text
- Ownership, policy and quality context travel with the answer
Day to day
One definition, wherever the question gets asked.
Ask the chatbot what "active user" means and get back the exact same definition your BI dashboard uses. Not a different number depending on who typed the question, and not a different number next quarter because someone updated a model prompt instead of the glossary.
What it's worth
What changes when AI answers from governed definitions.
“The creation of definitions for business terms, along with their interrelationships in dScribe, is vital to the success of our 'Chat with your data' AI copilot, allowing us to deliver highly accurate, actionable insights to our business partners.”
Give your AI a business brain, not a guess.
Twenty minutes, your own AI initiative, grounded in your own definitions.
What people usually ask at this point.
Does this replace our AI chatbot or agent platform?
No. dScribe supplies governed business context to the chatbot or agent platform you already use, so each AI use case does not need its own definitions and mappings.
What context does an assistant retrieve?
It can retrieve approved definitions, ownership, connected assets, policies and relevant quality context. The response is grounded in the same governed layer people use through search and BI.
How do we know what is ready for AI?
Context-readiness views show which fields and assets are connected to governed definitions and which still need an owner, relationship or approved meaning before an assistant depends on them.