Beyond the Model: Empower your AI with Data Grounding and Model Training

vytran

Microsoft

Dec 16, 2025

Why Stop at the Model?

With over 11,000 foundational, open, task-specific, and industry-tuned models, Foundry Models serve as the indispensable engine of Microsoft Foundry—empowering developers to build, optimize, and govern AI applications and agents with unmatched flexibility, speed, and contextual intelligence. Foundation models are powerful—but they don’t know your business. Without access to your data or workflows, they could hallucinate, misinterpret, and deliver results without context. Across industries, we see a clear pattern: customers who integrate enterprise knowledge and machine learning to their models unlock dramatically more value than those who don’t.

By combining Foundry Models with Foundry IQ and agent orchestration, organizations unlock faster time-to-value and up to 30% productivity gains—translating into hundreds of hours saved per day and significant reductions in AI development costs. This isn’t just about usage—it’s about outcomes. These customers move faster, scale wider, and deliver more accurate, secure, and useful AI experiences.

Level 1: Ground Your Models with Enterprise Knowledge

Retrieval-augmented generation (RAG) is the foundation of trustworthy AI. With Foundry IQ powered by Azure AI Search, customers index their own documents, policies, and data repositories, enabling models to answer with context and citations.

Accenture built a secure RAG framework using Azure AI Search to ground models on internal knowledge, helping global teams access accurate insights across industries while maintaining strict data governance.

Assembly Software used Azure AI Search to modernize its legal case management solution, enabling lawyers to retrieve case facts, filings, and precedents instantly, cutting hours of research time per case.

Some of our other best use cases:

A global manufacturing firm uses Azure AI Search to resolve regional policy conflicts by surfacing the right governance documents for each location.
A consulting firm built a summarization tool for 150-page accident reports using OCR from Azure Document Intelligence in Foundry Tools, Azure AI Search, and Azure OpenAI in Foundry Models—saving time and ensuring privacy compliance.
An engineering ISV created a multimodal knowledge retrieval system using Azure AI Search and Azure OpenAI, now being embedded into client-facing apps.

Level 2: Tune and Customize Models for Differentiated Outcomes

When general-purpose models aren’t enough, Azure Machine Learning helps teams build domain-specific intelligence. Gartner predicts that by 2027, 50% of data analysts will be retrained as data scientists, and data scientists will shift to AI engineering roles. Organizations will increasingly implement small, task-specific AI models, with usage volume at least three times higher than general-purpose large language models [1].

According to Gartner’s 2025 Magic Quadrant for Data Science and Machine Learning Platforms, Microsoft is recognized as a Leader for its innovation, breadth of capabilities, and ability to deliver business value through advanced AI and agentic workflows [1]. Azure ML in Microsoft Foundry empowers organizations to train, fine-tune, distill, and automatically upgrade models with minimal coding to optimize performance and costs.

Cognizant developed specialized machine learning models to automate software delivery and testing pipelines, reducing cycle times and boosting quality across thousands of enterprise projects.

ServiceTitan used Azure ML to build predictive models that forecast service demand for government clients, optimizing workforce scheduling and improving citizen response times.

YoungWilliams used Azure ML and Document Intelligence to automate public assistance case management, transforming manual workflows into intelligent pipelines. By training models to extract and classify structured data from thousands of forms and faxes, the organization now routes cases to the right departments automatically, accelerating response times and improving service delivery for families nationwide.

Some of our other best use cases:

A tech services company added ML-based feature extraction to unify messy point-of-sale data across distributors, enabling pattern recognition and analytics.
A healthcare startup used Azure ML and Document Intelligence to extract structured data from faxes and PDFs, routing claims to the correct internal systems.

Customers across industries—from healthcare to manufacturing to consulting—are proving that AI’s true power comes after the model. By attaching Azure ML and Foundry IQ by Azure AI Search, they’re transforming experiments into enterprise-grade solutions.

But the real value comes with agentic orchestration – bringing all the pieces together.

Level 3: Agentic Orchestration for Real Workflows

Foundry Agent Service enables multi-step workflows that combine model reasoning, data access, and business logic.

KPMG International built a custom AI agent within Agent Service to analyze regulatory and tax documents, surface relevant guidance, and help professionals deliver insights faster across global markets.

NTT DATA leveraged Microsoft Fabric data agents and Agent Service to orchestrate AI agents across multiple domains. These agents enable employees to interact with enterprise data, trigger insights, and automate decision workflows—all grounded in trusted corporate data. The result: over 50% faster time to market and heightened productivity across functions.

Some of our other best use cases:

A financial services firm built a custom agent that crawls SharePoint and file shares to answer business queries and perform transactions. It uses Foundry IQ, Cosmos DB, and Copilot Studio for orchestration.

A consulting firm automated invoice processing by extracting data from emails, routing it through Document Intelligence, and posting to their ERP system.

A manufacturing enterprise is mapping 30 years of product nomenclature using agents to unify terminology across systems.

Level 4: Communication & Customer Interaction

Agents aren’t just for backend tasks—they’re transforming customer-facing workflows.

Fujitsu used Foundry to power copilots for customer support, enabling real-time answers and proactive recommendations across multiple regions and languages.

Audi deployed an intelligent assistant in just two weeks to answer internal and external queries by retrieving information from Microsoft SharePoint and Yammer, enabling real-time, contextually grounded responses across the organization.

Other best use cases:

A manufacturing company is automating responses to customer emails by connecting ERP, SQL Server, and Oracle DBs through Microsoft Fabric and building intelligence layers on top.

Another firm is piloting a model to assist customer reps, identifying cases where the model struggles and iterating for improvement.

Guidance for AI Development Teams Just Getting Started

Orchestrate with open, flexible frameworks: Use Foundry Agent Service to streamline development and automate tasks.
Add models to support your use case: Benchmark and select from over 11,000 foundational, open, reasoning, multimodal, and industry-specific models – including models from OpenAI and Anthropic.
Empower agents to understand business context: Use Foundry IQ to securely ground AI apps and agents on your data stored in any location (including SharePoint, OneLake, blob, and more).
Enable AI to act on your business systems: Automate workflows with pre-built and customizable MCP tools for tasks such as OCR, translation, and speech – and enable AI to respond with real-time precision across 1400+ business systems like SAP, Salesforce, and Dynamics 365.
Customize and upgrade models with ease: Use Azure Machine Learning for predictions, classifications, and custom logic.
Continuously govern the AI lifecycle with organization-wide observability and controls: Foundry Control Plane to gain fleet-wide monitoring and governance with configurable evaluations and seamless CI/CD integration.