Your AI is only as good as the definitions underneath it
Actian anchors every AI query to a governed semantic layer, so agents and analysts get consistent, policy-aware answers, not raw schema access.
One luxury automotive group. 10,000 governed definitions.
Automated definitions
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Zero-handshake self-service dashboards
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Multi-platform agentic workflows validated
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One of Europe's largest automotive importers stopped its AI from hallucinating. By giving it governed business context instead of raw data.
Our customer is one of the largest family-owned multinational automotive luxury importers in the Netherlands. €10B+ in annual revenue. Operations spanning multiple countries.
Their AI problem was specific: they were generating dashboards with generative AI fast, but the outputs couldn’t be trusted. Different business units used different definitions for the same metrics. AI agents couldn’t execute fuzzy searches without an exact database ID. And there was no standardized way to connect LLMs to their downstream BI tools.
The challenge
- Rapid AI-generated dashboards risked surfacing unverified, unanchored data to decision-makers.
- Fragmented metric definitions across business units triggered constant disputes over numbers instead of decisions.
- Early AI agents required exact field IDs or database names, with no ability to search by meaning.
- No unified protocol to connect LLMs directly to Tableau and other BI tools.
What Actian did
- Deployed Actian’s MCP Server as a unified, decoupled communication layer, connecting AI agents to governed business logic without vendor lock-in.
- Connected the core data catalog directly to LLMs as an active business glossary, translating natural language requests into governed structured definitions and eliminating hallucinations at source.
- Co-designed next-generation AI Analyst semantic search capabilities that surface conceptually relevant variables based on meaning, not exact keyword matches.
What they achieved
10,000+ business definitions auto-populated from the data warehouse and GitHub code repositories into their global catalog.
Zero-handshake self-service dashboards investigated using Gemini to programmatically write and modify Tableau XML layout code directly from governed metadata.
Multi-platform agentic workflows validated across N8N and data science pipelines, bridging natural language queries to real-time dealership figures.
Built for AI that needs to be right, not just fast
A semantic layer that stays current
The Federated Knowledge Graph operationalizes business definitions, lineage, and policy context across your data estate. The Data Steward Agent continuously synchronizes those definitions as data changes. AI output doesn’t drift because the semantic layer doesn’t.
Governance that travels with every AI query
Governance context flows into every AI query at runtime, not just at ingestion. MCP Server and A2A protocols let third-party AI agents query governed business logic via open standards. Agents get policy-aware context at the point of query, governed by the same definitions your data stewards maintain.
Lineage that traces AI answers back to source
Every AI output is traceable to the governed asset that produced it. Business and technical lineage connect, so governance teams can validate what the AI used before they trust what it said.
Recognize any of these?
If your team is dealing with this… |
Actian addresses it with… |
|---|---|
AI dashboards producing numbers nobody agrees on |
Semantic layer anchors every output to a single governed definition |
Different teams calling the same metric different things |
Federated Knowledge Graph enforces one definition, everywhere |
AI agents failing without exact field IDs or database names |
Semantic search surfaces relevant variables by meaning, not keyword |
No audit trail for what data an AI agent actually used |
Smart Lineage traces every AI output back to the governed source asset |
Where AI readiness connects to the bigger picture
Ready to give your AI something it can trust?
Talk to a solutions engineer about your AI stack. We’ll show you how the semantic layer maps to your specific agents, tools, and governance requirements.
Book a Solutions Call
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