We prepare your data for trustworthy AI answers
So your business can ask questions in natural language and receive answers based on data it understands and can trust. We take care of data models, metadata and governance so AI-powered querying works securely with the data you already have.
What you gain from AI-ready data
AI built on company data only makes sense when its answers are based on the right models, metrics and permissions. We prepare the data foundation so users can ask questions in natural language and receive answers they can trust.
Users no longer need to click through reports or search for the right dashboard. They ask a question in natural language and receive an answer based on approved data.
AI must follow the same rules as reporting. We work with permissions, RLS/OLS, sensitivity labels and governance.
We prepare semantic models, metadata and verified answers so AI can better understand company prompts, metrics and internal context.
You are not building another isolated tool. You extend the value of the data layer, reporting and semantic models you already have in place.
We build a stable foundation for trustworthy AI answers based on your data
1. Readiness assessment
We review data models, licensing, capacity and tenant settings, including governance. In other words, everything that creates the foundation for effective AI use across the company.
2. Review of semantic models
We review the model structure, relationships, metrics, names, descriptions, hierarchies and business logic.
3. Preparing data for AI
We prepare an AI data schema, verified answers, AI instructions and metadata to improve the accuracy of answers based on your data
4. Security and governance
We configure permissions, RLS/OLS, sensitivity labels, endorsement and rules for approved models.
5. User testing
We test priority questions, synonyms, edge-case queries and answer consistency.
6. Production deployment
We prepare the deployment, operating rules, capacity monitoring and further iterations based on real-world usage.
How AI will work with your data after deployment
Natural-language questions about your data
People ask questions in everyday language and receive answers based on an approved Power BI model.
Trustworthy answers
Outputs are based on prepared metrics, descriptions, verified answers and business logic.
Faster data exploration
There is no need to search for the right report, page or filter. AI helps users find relationships in the data faster.
Secure use of AI
AI respects permissions, RLS/OLS and sensitivity labels just like standard reporting.
Why prepare your data for AI with intecs
We know the Microsoft data stack
We treat Power BI, MS Fabric, semantic models, governance and operations as one connected ecosystem.
We connect data with business
We help select the questions, KPIs and scenarios where AI can deliver real value from your data.
We build on a secure foundation
We work with permissions, governance and delivery standards. We are certified to ISO 27001 and ISO 9001.
We prepare solutions for production
We set up testing, monitoring, adoption and ongoing optimisation based on real-world usage.
Find out whether your data is ready for AI
Book an AI readiness assessment workshop. We review your data models, governance and technical setup and propose specific steps for secure AI-powered querying across your company data.
During the workshop, you will receive:
- a quick assessment of your data’s readiness for AI
- an overview of the main gaps in your data and models
- recommendations for improving the semantic layer
- a proposal for a secure pilot based on a specific scenario
- clear next steps for deploying AI-powered querying
Frequently asked questions about AI readiness
Not always. For AI-powered querying over Power BI data, the key requirements are mainly the Power BI/Fabric environment, capacity and tenant configuration. As part of the assessment, we verify what you already have in place and what still needs to be added.
Not in every scenario. Fabric is a suitable path if you want to develop AI over your data in the long term and connect reporting, semantic models, governance and your data platform.
This is a common starting point. We begin with an assessment, evaluate semantic models, metadata, metrics, permissions and data quality, and propose specific improvements.
We test priority business questions, alternative phrasing, edge-case queries and output consistency. The preparation includes high-quality metadata, an AI data schema, verified answers and clear business logic.
Security depends on properly configured governance. We address permissions, RLS/OLS, sensitivity labels, endorsement and rules for models approved for AI use.