AI Data Management and Analytics 

Effective AI requires trusted, well-governed data as its foundation. Our team will: 

• Establish data governance policies that define ownership, access controls, and quality standards using Microsoft Purview's unified catalog, sensitivity labels, and Data Security Posture Management (DSPM) for AI 

• Implement data quality assessment processes, including drift, duplication, and freshness checks, so issues are identified and remediated before they reach any downstream AI system 

• Create end-to-end data lineage using Purview's automatic lineage graphs across Fabric, SharePoint, OneDrive, Azure SQL, and third-party sources, so you can always answer where a given AI answer came from 

Proper data architecture is what allows AI systems to access and process information efficiently. We help: 

• Design and implement unified data estates anchored in OneLake, with zero-copy shortcuts to AWS S3, Google Cloud Storage, Azure Data Lake Storage, Dataverse, and on-premises sources via Fabric Data Gateway, eliminating duplication while keeping data residency requirements under control 

• Build the pipelines required for generative AI workloads: document ingestion and parsing with Azure AI Document Intelligence and Azure AI Content Understanding, chunking, embedding generation, and indexing into Azure AI Search and Foundry IQ knowledge bases 

• Apply a medallion architecture (Bronze / Silver / Gold) that aligns operational, analytical, and AI-grounding data layers so every AI-powered query, from Microsoft 365 Copilot to custom Foundry agents, runs on current, trusted information 

Understanding your data quality and usage patterns is what drives continuous improvement. Our experts will: 

• Stand up conversational analytics using Copilot in Power BI, Fabric Data Agent, and custom Microsoft Foundry agents so business stakeholders can ask natural-language questions and get governed, cited answers from OneLake 

• Develop Power BI dashboards that monitor data quality metrics, AI usage, grounding scores, and pipeline health 

• Create feedback loops that continuously improve data quality, retrieval performance, and answer groundedness using Foundry observability signals and business outcomes 

AI systems require consistent, integrated data across multiple sources. We help: 

• Map and integrate data from disparate systems — SAP, Salesforce, Dynamics 365, Azure Databricks, Snowflake, and line-of-business databases — into OneLake using Fabric's zero-copy mirroring and bi-directional shortcuts 

• Standardize data definitions, formats, and business meaning using Fabric IQ ontologies so agents and reports share a single understanding of customer, revenue, claim, or asset 

• Establish master data management practices that keep data consistent as your AI estate, and its appetite for grounded context, grows