Chat-with-Data on Azure

We build governed chat-with-data experiences on Azure so business teams can ask plain-language questions and get traceable answers from the right data sources. This fits when you already have useful data in a data lake, warehouse, analytical database, BI layer, or APIs, but getting an answer still depends on analysts, SQL skills, or manual reporting work.

What we build

We design the answer path end to end. That includes how a question is interpreted, which data model or query endpoint is used, how business rules are applied, and how the answer is shown back to the user. Depending on the case, we connect the experience to analytical storage, SQL query endpoints, BI models, APIs, and selected document sources when business context lives outside structured tables.

The output is built for decisions, not curiosity. Answers stay concise. Numbers match agreed definitions. Users can see where the answer came from and drill into the records behind it. When a question is ambiguous or outside scope, the service says so clearly instead of guessing.

How the engagement runs

We start with a short design phase around real questions from real users. That gives us the first use cases, the metric definitions, and the access model. After that we build the production path in short iterations and validate it against agreed example questions and expected answers.

Before go-live, we test response quality, latency, access boundaries, and operational visibility. We also hand over the prompts, evaluation set, monitoring approach, and required platform changes so your team can keep improving the service with confidence. If your data platform still needs work, we can connect this delivery with our Cloud Data Platform and Microsoft AI Foundry & Azure OpenAI services.

Key Technologies

• Azure OpenAI Service for question understanding and answer orchestration
• Data lake, warehouse, or analytical storage for governed access to business data
• Transformation and modeling layers such as dbt, semantic models, or query endpoints
• Azure SQL Database or Databricks SQL when structured operational data needs direct access
• Azure AI Search when document snippets or knowledge-base content need to complement tabular answers
• Power BI and embedded drill-through views for validation and follow-up
• Microsoft Entra ID, managed identities, and existing RBAC or RLS controls

Delivery Foundations

• A scoped question set with named business owners and acceptance criteria
• Metric and data-model review so the assistant speaks the same language as reporting
• Query and answer traceability down to source tables, measures, or records
• Access propagation from Entra ID and underlying data platforms instead of separate shadow permissions
• Evaluation against known business questions before release and after each change
• Usage telemetry for unanswered questions, slow paths, and costly query patterns