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AI data foundation

We unify data, set up governance, semantics, and secure access so that analytics, planning, AI, and agents work on a verified data foundation, not on chaos across systems.

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Problem

AI pilots launch quickly, but unprepared data prevents scaling

AI lacks clear context, semantics, and verified sources

Data is spread across systems, teams, and countries

AI without secure access to data

Solution

Trusted data foundation for AI,
analytics, and decision-making

Unified data across systems

We unify data from different systems, countries, and teams into an environment with clear structure, rules, and responsibilities. Instead of isolated databases, spreadsheets, and local solutions, you get an environment where everyone works with the same context.

Anchor your metrics
and the meaning of data

Create a Single Source of Truth with clear definitions, metadata and business logic, so AI agents as well as analytics, dashboards or planning models all work with the same trusted numbers and business context.

Build a secure foundation for AI

We prepare your data architecture so that AI models and agents can work with verified company data, understand the context, and access only what they truly need.

Semantics that gives
AI business meaning

AI doesn’t just need to know the name of a field in a database. It needs to understand what the data means for your business. That’s why we prepare metadata, metric definitions, and a semantic layer.

Do you have a process where data or AI is not delivering the expected impact?

Discuss your specific situation with an expert

Use cases

Data foundation
for scaling AI

  • Data preparation for multiple AI scenarios at once
  • Verified data with clear context for trustworthy AI answers
  • A secure foundation for AI projects with measurable impact

Connecting structured and unstructured data

  • Connecting BI numbers with context from internal documents
  • Using audits, contracts, or policies for more accurate answers
  • Explaining not only what is happening, but also why

AI assistants
for company data

  • Lower risk of inaccurate answers thanks to semantic layer and governance
  • Answers in your business context, not in technical terminology
  • Ask questions about data in natural language

Data foundation for process automation

  • Automation of repeatable processes with clear rules and approvals
  • Clear instructions for agents on when to trigger the next action
  • Use across finance, HR, sales, operations, or internal services

Secure connection of AI agents to company data

  • Connect AI models without moving data outside your platform
  • MCP as a standardized layer between AI agents and data platforms
  • Access only to verified, permitted, and relevant data

AI agents for analysis and decision-making

  • Data analysis, pattern detection, and business-ready outputs
  • Connecting agents to data products through a governed data platform
  • Use different AI models without technology lock-in

Technology adoption for its own sake has never created value, which is also true with gen AI.

McKinsey & Company

Let’s solve your AI data challenges

If you are considering AI or dealing with data quality and how data is used in decision-making, we will define the next step together — one that makes sense in the context of your business.