Integrate Custom Azure AI Agents with CoPilot Studio and M365 CoPilot
Integrating Custom Agents with Copilot Studio and M365 Copilot In today's fast-paced digital world, integrating custom agents with Copilot Studio and M365 Copilot can significantly enhance your company's digital presence and extend your CoPilot platform to your enterprise applications and data. This blog will guide you through the integration steps of bringing your custom Azure AI Agent Service within an Azure Function App, into a Copilot Studio solution and publishing it to M365 and Teams Applications. When Might This Be Necessary: Integrating custom agents with Copilot Studio and M365 Copilot is necessary when you want to extend customization to automate tasks, streamline processes, and provide better user experience for your end-users. This integration is particularly useful for organizations looking to streamline their AI Platform, extend out-of-the-box functionality, and leverage existing enterprise data and applications to optimize their operations. Custom agents built on Azure allow you to achieve greater customization and flexibility than using Copilot Studio agents alone. What You Will Need: To get started, you will need the following: Azure AI Foundry Azure OpenAI Service Copilot Studio Developer License Microsoft Teams Enterprise License M365 Copilot License Steps to Integrate Custom Agents: Create a Project in Azure AI Foundry: Navigate to Azure AI Foundry and create a project. Select 'Agents' from the 'Build and Customize' menu pane on the left side of the screen and click the blue button to create a new agent. Customize Your Agent: Your agent will automatically be assigned an Agent ID. Give your agent a name and assign the model your agent will use. Customize your agent with instructions: Add your knowledge source: You can connect to Azure AI Search, load files directly to your agent, link to Microsoft Fabric, or connect to third-party sources like Tripadvisor. In our example, we are only testing the CoPilot integration steps of the AI Agent, so we did not build out additional options of providing grounding knowledge or function calling here. Test Your Agent: Once you have created your agent, test it in the playground. If you are happy with it, you are ready to call the agent in an Azure Function. Create and Publish an Azure Function: Use the sample function code from the GitHub repository to call the Azure AI Project and Agent. Publish your Azure Function to make it available for integration. azure-ai-foundry-agent/function_app.py at main · azure-data-ai-hub/azure-ai-foundry-agent Connect your AI Agent to your Function: update the "AIProjectConnString" value to include your Project connection string from the project overview page of in the AI Foundry. Role Based Access Controls: We have to add a role for the function app on OpenAI service. Role-based access control for Azure OpenAI - Azure AI services | Microsoft Learn Enable Managed Identity on the Function App Grant "Cognitive Services OpenAI Contributor" role to the System-assigned managed identity to the Function App in the Azure OpenAI resource Grant "Azure AI Developer" role to the System-assigned managed identity for your Function App in the Azure AI Project resource from the AI Foundry Build a Flow in Power Platform: Before you begin, make sure you are working in the same environment you will use to create your CoPilot Studio agent. To get started, navigate to the Power Platform (https://make.powerapps.com) to build out a flow that connects your Copilot Studio solution to your Azure Function App. When creating a new flow, select 'Build an instant cloud flow' and trigger the flow using 'Run a flow from Copilot'. Add an HTTP action to call the Function using the URL and pass the message prompt from the end user with your URL. The output of your function is plain text, so you can pass the response from your Azure AI Agent directly to your Copilot Studio solution. Create Your Copilot Studio Agent: Navigate to Microsoft Copilot Studio and select 'Agents', then 'New Agent'. Make sure you are in the same environment you used to create your cloud flow. Now select ‘Create’ button at the top of the screen From the top menu, navigate to ‘Topics’ and ‘System’. We will open up the ‘Conversation boosting’ topic. When you first open the Conversation boosting topic, you will see a template of connected nodes. Delete all but the initial ‘Trigger’ node. Now we will rebuild the conversation boosting agent to call the Flow you built in the previous step. Select 'Add an Action' and then select the option for existing Power Automate flow. Pass the response from your Custom Agent to the end user and end the current topic. My existing Cloud Flow: Add action to connect to existing Cloud Flow: When this menu pops up, you should see the option to Run the flow you created. Here, mine does not have a very unique name, but you see my flow 'Run a flow from Copilot' as a Basic action menu item. If you do not see your cloud flow here add the flow to the default solution in the environment. Go to Solutions > select the All pill > Default Solution > then add the Cloud Flow you created to the solution. Then go back to Copilot Studio, refresh and the flow will be listed there. Now complete building out the conversation boosting topic: Make Agent Available in M365 Copilot: Navigate to the 'Channels' menu and select 'Teams + Microsoft 365'. Be sure to select the box to 'Make agent available in M365 Copilot'. Save and re-publish your Copilot Agent. It may take up to 24 hours for the Copilot Agent to appear in M365 Teams agents list. Once it has loaded, select the 'Get Agents' option from the side menu of Copilot and pin your Copilot Studio Agent to your featured agent list Now, you can chat with your custom Azure AI Agent, directly from M365 Copilot! Conclusion: By following these steps, you can successfully integrate custom Azure AI Agents with Copilot Studio and M365 Copilot, enhancing you’re the utility of your existing platform and improving operational efficiency. This integration allows you to automate tasks, streamline processes, and provide better user experience for your end-users. Give it a try! Curious of how to bring custom models from your AI Foundry to your CoPilot Studio solutions? Check out this blogWhat’s new in Azure AI Foundry Fine Tuning
Discover the latest advancements in Azure AI Foundry, including Reinforcement Fine-Tuning with o4-mini, Global Training, and the Developer Tier. These new capabilities enhance versatility, accessibility, and scalability for developers and enterprises. Stay tuned for more announcements and technical deep dives throughout Build 2025!The Future of AI: How Lovable.dev and Azure OpenAI Accelerate Apps that Change Lives
Discover how Charles Elwood, a Microsoft AI MVP and TEDx Speaker, leverages Lovable.dev and Azure OpenAI to create impactful AI solutions. From automating expense reports to restoring voices, translating gestures to speech, and visualizing public health data, Charles's innovations are transforming lives and democratizing technology. Follow his journey to learn more about AI for good.The Future of AI: Autonomous Agents for Identifying the Root Cause of Cloud Service Incidents
Discover how Microsoft is transforming cloud service incident management with autonomous AI agents. Learn how AI-enhanced troubleshooting guides and agentic workflows are reducing downtime and empowering on-call engineers.
Your First GraphRAG Demo - A Video Walkthrough
Overview Graph Retrieval-Augmented Generation (GraphRAG) is an AI approach that combines knowledge graphs with retrieval-augmented generation to deliver rich, context- and relationship-aware answers from complex data. GraphRAG is a Microsoft Research - Project that has, after its initial publication, gained significant community support but has not, to date, been converted to a productized Like all methodologies, it's use should be purposeful. To determine if GraphRAG is right for your use case, consider your applications needs to consume RAG outcomes. Review the questions and Analysis Guide pictured below. Does your use case have a lot of duplicate information? Can questions be answered based on only some of the relevant knowledge? What is the scale of your solution? For a single question, would tens of knowledge chunks be relevant or tens of thousands of chunks? What is your use case tolerance for hallucinations (i.e., how critical is quality, which implies a necessity to retrieve all relevant knowledge)? Once you have determined your required RAG consumption pattern, you can more easily map methodologies to this. The above patterns are mapped below to sample AI methodologies. GraphRAG is highlighted as a solution when all relevant chunks might be too large for a single context window and also where all relevant or all chunks are required to be retrieved. For more information about GraphRAG and its use case appropriateness, see the below: Microsoft Research - Project GraphRAG Tech Community: The Future of AI: GraphRAG – A better way to query interlinked documents Tech Community: Unlocking Insights: GraphRAG & Standard RAG in Financial Services After GraphRAG selection There are different implementations and GitHub repositories available for GraphRAG concepts. Since Microsoft Research's inaugural publication in April 2024, different variations of the GraphRAG approach have been published. It is recommended to start your experimentations with the core GraphRAG GitHub Page and GraphRAG GitHub Repository. Once you've finished an initial, local proof of concept on a real-world use case and like your outcomes, you can move towards industrialization. See the GitHub Azure-Samples/graphrag-accelerator for a one-click deployment industrialization path. Standing up the most popular use case: The Research Assistant GraphRAG does particularly well as a research assistant with large amounts of data. It is able to analyze data, draw meaningful connections, and synthesize concepts and patterns into an insightful outcome. This section walks you through using graphrag python library with Azure OpenAI on top of a limited number of Wikipedia articles relating to financial auditing. The associated GitHub repository for this section is: adhazel/graphrag_demo. Running the Demo Complete the steps in each of the below locations, and, optionally, follow along in the video. GitHub Local Environment Setup Use Case A Research Assistant notebook After the Demo This video walk through is, by necessity, the short, happy path. Here are some ideas on what to check out next - be sure to watch the video for a full walkthrough of the GraphRAG Visualization Guide. Perfect the global, local, and drift searches Tune the prompts, paying special attention to extract graph, extract claims, and community report prompts Dig into and fine tune the GraphRAG settings (the yaml) Set up a visualization tool on top of your graph Explore a production use case! Many thanks for your attention and happy coding!Unlocking Document Intelligence: Mistral OCR Now Available in Azure AI Foundry
Every organization has a treasure trove of information—buried not in databases, but in documents. From scanned contracts and handwritten forms to research papers and regulatory filings, this knowledge often sits locked in static formats, invisible to modern AI systems. Imagine if we could teach machines not just to read, but to truly understand the structure and nuance of these documents. What if equations, images, tables, and multilingual text could be seamlessly extracted, indexed, and acted upon—at scale? That future is here. Today we are announcing the launch of Mistral OCR in the Azure AI Foundry model catalog—a state-of-the-art Optical Character Recognition (OCR) model that brings intelligent document understanding to a whole new level. Designed for speed, precision, and multilingual versatility, Mistral OCR unlocks the potential of unstructured content with unmatched performance. From Patient Charts to Investment Reports—Built for Every Industry Mistral OCR’s ability to extract structure from complex documents makes it transformative across a range of verticals: Healthcare Hospitals and health systems can digitize clinical notes, lab results, and patient intake forms, transforming scanned content into structured data for downstream AI applications—improving care coordination, automation, and insights. Finance & Insurance From loan applications and KYC documents to claims forms and regulatory disclosures, Mistral OCR helps financial institutions process sensitive documents faster, more accurately, and with multilingual support—ensuring compliance and improving operational efficiency. Education & Research Academic institutions and research teams can turn PDFs of scientific papers, course materials, and diagrams into AI-readable formats. Mistral OCR’s support for equations, charts, and LaTeX-style formatting makes it ideal for scientific knowledge extraction. Legal & Government With its multilingual and high-fidelity OCR capabilities, legal teams and public agencies can digitize contracts, historical records, and filings—accelerating review workflows, preserving archival materials, and enabling transparent governance. Key Highlights of Mistral OCR According to Mistral their OCR model stands apart due to the following: State-of-the-Art Document Understanding Mistral OCR excels in parsing complex, multimodal documents—extracting tables, math, and figures with markdown-style clarity. It goes beyond recognition to deliver understanding. benchmark testing. Whether you’re working in Hindi, Arabic, French, or Chinese—this model adapts seamlessly. State-of-the-Art Document Understanding Mistral OCR excels in parsing complex, multimodal documents—extracting tables, math, and figures with markdown-style clarity. It goes beyond recognition to deliver understanding. Multilingual by Design With support for dozens of languages and scripts, Mistral OCR achieves 99%+ fuzzy match scores in benchmark testing. Whether you’re working in Hindi, Arabic, French, or Chinese—this model adapts seamlessly. Fastest in Its Class Process up to 2,000 pages per minute on a single node. This speed makes it ideal for enterprise document pipelines and real-time applications. Doc-as-Prompt + Structured Output Turn documents into intelligent prompts—then extract structured, JSON-formatted output for downstream use in agents, workflows, or analytics engines. Why use Mistral OCR on Azure AI Foundry? Mistral OCR is now available as serverless APIs through Models as a Service (MaaS) in Azure AI Foundry. This enables enterprise-scale workloads with ease. Network Isolation for Inferencing: Protect your data from public network access. Expanded Regional Availability: Access from multiple regions. Data Privacy and Security: Robust measures to ensure data protection. Quick Endpoint Provisioning: Set up an OCR endpoint in Azure AI Foundry in seconds. Azure AI ensures seamless integration, enhanced security, and rapid deployment for your AI needs. How to deploy Mistral OCR model in Azure AI Foundry? Prerequisites: If you don’t have an Azure subscription, get one here: https://azure.microsoft.com/en-us/pricing/purchase-options/pay-as-you-go Familiarize yourself with Azure AI Model Catalog Create an Azure AI Foundry hub and project. Make sure you pick East US, West US3, South Central US, West US, North Central US, East US 2 or Sweden Central as the Azure region for the hub. Create a deployment to obtain the inference API and key: Open the model card in the model catalog on Azure AI Foundry. Click on Deploy and select the Pay-as-you-go option. Subscribe to the Marketplace offer and deploy. You can also review the API pricing at this step. You should land on the deployment page that shows you the API and key in less than a minute. These steps are outlined in detail in the product documentation. From Documents to Decisions The ability to extract meaning from documents—accurately, at scale, and across languages—is no longer a bottleneck. With Mistral OCR now available in Azure AI Foundry, organizations can move beyond basic text extraction to unlock true document intelligence. This isn’t just about reading documents. It’s about transforming how we interact with the knowledge they contain. Try it. Build with it. And see what becomes possible when documents speak your language.Unlock Multi-Modal Embed 4 and Multilingual Agentic RAG with Command A on Azure
Developers and enterprises now have immediate access to state-of-the-art generative and semantic models purpose-built for RAG (Retrieval-Augmented Generation) and agentic AI workflows on Azure AI Foundry to: Deploy high-performance LLMs and semantic search engines directly into production Build faster, more scalable, and multilingual RAG pipelines Leverage models that are optimized for enterprise workloads in finance, healthcare, government, and manufacturing Cohere Embed 4: High-Performance Embeddings for Search & RAG Accompanying Command A is Cohere’s Embed 4, a cutting-edge embedding model ideal for retrieval-augmented generation pipelines and semantic search. Embed 4 (the latest evolution of Cohere’s Embed series) converts text – and even images – into high-dimensional vector representations that capture semantic meaning. It’s a multi-modal, multilingual embedding model designed to provide recall and relevance in vector search, text classification, and clustering tasks. What makes Embed 4 stand out? 100+ Language Support: This model is truly global – it supports well over 100 languages for text embeddings. You can encode queries and documents in many languages (Arabic, Chinese, French, Hindi, etc.) into the same vector space, enabling cross-lingual search out of the box. For example, a question in Spanish can retrieve a relevant document originally in English if their ideas align semantically. Multi-Modal Embeddings: Embed 4 is capable of embedding not only text but also images. This means you can use it for multimodal search scenarios – e.g. indexing both textual content and images and allowing queries across them. Under the hood, the model has an image encoder; the Azure AI Foundry SDK provides an ImageEmbeddingsClient to generate embeddings from images. With this, you could embed a diagram or a screenshot and find text documents that are semantically related to that image’s content. Matryoshka Embeddings (Scalable Dimensions): A novel feature in Cohere’s Embed 4 is Matryoshka Representation Learning, which produces embeddings that can be truncated to smaller sizes with minimal loss in fidelity. In practice, the model can output a high-dimensional vector (e.g. 768 or 1024 dims) but you have the flexibility to use just the first 64, 128, 256, etc. dimensions if needed. These “nested” embeddings mean you can choose a vector size that balances accuracy vs. storage/query speed – smaller vectors save memory and compute while still preserving most of the semantic signal. This is great for enterprise deployments where vector database size and latency are critical. Enterprise Optimizations: Cohere has optimized Embed 4 for production use. It supports int8 quantization and binary embedding output natively, which can drastically reduce storage footprint and speed up similarity search with only minor impact on accuracy (useful for very large indexes). The model is also trained on massive datasets (including domain-specific data) to ensure robust performance on noisy enterprise text. It achieves state-of-the-art results on benchmark evaluations like MTEB, meaning you get retrieval quality on par with or better than other leading embeddings models (OpenAI, Google, etc.). For instance, Cohere’s previous embed model was top-tier on cross-language retrieval tasks and Embed4 further improves on that foundation. Cohere Command A: Generative Model for Enterprise AI Command A is Cohere’s latest flagship large language model, designed for high-performance text generation in demanding enterprise scenarios. It’s an instruction-tuned, conversational LLM that excels at complex tasks like multi-step reasoning, tool use (function calling), and retrieval-augmented generation. Command A features a massive 111B parameter Transformer architecture with 256K token context length – enabling it to handle extremely large inputs (hundreds of pages of text) in a single prompt without losing coherence. Source for above benchmarks : Introducing Command A: Max performance, minimal compute Some key capabilities of Command A include: Long Context (256K tokens): Using an innovative attention architecture (sliding window + global attention), Command A can ingest up to 256,000 tokens of text in one go. This enables use cases like analyzing lengthy financial reports or entire knowledge bases in a single prompt. Enterprise-Tuned Generation: Command A is optimized for business applications – it’s excellent at instructions, summarization, and especially RAG workflows where it integrates retrieved context and even cites sources to mitigate hallucinations. It supports tool calling (function calling) out-of-the-box so it can interact with external APIs or data sources as part of an Azure AI Agent. Multilingual Proficiency: Command A is good at multilingual use cases (covering all major business languages, with near leading performance in Japanese, Korean, and German). Efficient Deployment: Despite its size, Command A is engineered for efficiency – it delivers 150% higher throughput than its predecessor (Command R+ 08-2024) and requires only 2× A100/H100 GPUs to run. In practice this means lower latency. It also supports streaming token output, so applications can start receiving the response as it’s generated, keeping interaction latency low. Real-World Use Cases for Command A + Embed 4 With both a powerful generative model and a state-of-the-art embedding model at your fingertips, developers can build advanced AI solutions. Here are some real-world use cases unlocked by Command A and Embed 4 on Azure: Financial Report Summarization (RAG): Imagine ingesting thousands of pages of financial filings, earnings call transcripts, and market research into a vector store. Using Embed 4, you can embed and index all this text. When an analyst asks “What were the key revenue drivers mentioned in ACME Corp’s Q1 2025 report?”, you use the query embedding to retrieve the most relevant passages. Command A (with its 256K context) can then take those passages and generate a concise summary or answer with cited evidence. The model’s long context window means it can consider all retrieved chunks at once, and its enterprise tuning ensures factual, business-appropriate summaries. Legal Research Agent (Tool Use + Multilingual): For example a multinational law firm handling cross-border mergers and acquisitions. They have a vast repository of legal documents in multiple languages. Using Embed 4, they index these documents, creating multilingual embeddings. When a lawyer researches a specific legal precedent related to a merger in Germany, they can query in English. Embed 4 retrieves relevant German documents, and Command A summarizes key points, translates excerpts, and compares legal arguments across jurisdictions. Furthermore, Command A leverages tool calling (utilizing agentic capabilities) to retrieve additional information from external databases, such as company registration details and regulatory filings, integrating this data into its analysis to provide a comprehensive report. Technician Knowledge Assistant (RAG + Multilingual): Think of a utilities company committed to operational excellence, managing a vast network of critical equipment, including power generators, transformers, and distribution lines. They can leverage Command A, integrated with Embed 4, to index a comprehensive repository of equipment manuals, maintenance records, and sensor data in multiple languages. This enables technicians and engineers to access critical knowledge instantly. Technicians can ask questions in their native language about specific equipment issues, and Command A retrieves relevant manuals, troubleshooting guides, and past repair reports. It also guides technicians through complex maintenance procedures step-by-step, ensuring consistency and adherence to best practices. This empowers the company to optimize maintenance processes, improve overall equipment reliability, and enhance communication, ultimately achieving operational excellence. Multimodal Search & Indexing: With Embed 4’s image embedding capability, you can build search systems that go beyond text. For instance, a media company could index their image library by generating embeddings for each image (using Azure’s Image Embeddings client) and also index captions/descriptions. A user could then supply a query image (or a textual description) and retrieve both images and articles that are semantically similar to the query. This is useful for scenarios like finding slides similar to a given diagram, searching scanned invoices by content, or matching user-uploaded photos to reference documents. Getting Started: Deploying via Azure AI Foundry In Azure AI Foundry, Embed 4 can be used via the Embeddings API to encode text or images into vectors. Each text input is turned into a numeric vector (e.g. 1024-dimension float array) that you can store in a vector database or use for similarity comparisons. The embeddings are normalized for cosine similarity by default. You can also take advantage of Azure’s vector index or Azure Cognitive Search to directly build vector search on top of these model outputs. Image Source : Introducing Embed 4: Multimodal search for business One of the biggest benefits of using Azure AI Foundry is the ease of deployment for these models. Cohere’s Command A and Embed 4 are available in the model catalog – you can find their model cards and deploy them in just a few clicks. Azure Foundry supports serverless API endpoints for these models, meaning Microsoft hosts the inference infrastructure and scales it for you (with pay-as-you-go billing). Integration with Azure AI Agent Service: If you’re building an AI agent (a system that can orchestrate models and tools to perform tasks), Azure AI Agent Service makes it easy to incorporate these models. In the Agent Service, you can simply reference the deployed model by name as the agent’s reasoning LLM. For example, you could specify an agent that uses CohereCommandA as its model, and add tools like Azure Cognitive Search. The agent can then handle user requests by, say, using a Search tool (powered by Embed 4 vector index) and then passing the results to Command A for answer formulation – all managed by the Azure Agent framework. This lets you build production-grade agentic AI workflows that leverage Cohere’s models with minimal plumbing. In short, Azure provides the glue to connect Command A + Embed 4 + Tools into a coherent solution. Try Command A and Embed 4 today on Azure AI Foundry The availability of Cohere’s Command A and Embed 4 on Azure AI Foundry empowers developers to build the next generation of intelligent apps on a fully managed platform. You can now easily deploy a 256K-context LLM that rivals the best in the industry, alongside a high-performance embedding model that plugs into your search and retrieval pipelines. Whether it’s summarizing lengthy documents with cited facts, powering a multilingual enterprise assistant, enabling multimodal search experiences, or orchestrating complex tool-using agents – these models open up a world of possibilities. Azure AI Foundry makes it simple to integrate these capabilities into your solutions, with the security, compliance, and scalability of Azure’s cloud. We encourage you to try out Command A and Embed 4 in your own projects. Spin them up from the Azure model catalog, use the provided SDK examples to get started, and explore how they can elevate your applications’ intelligence. With Cohere’s models on Azure, you have cutting-edge AI at your fingertips, ready to deploy in production. We’re excited to see what you build with them!Expanding the Llama 4 Herd: New Models Now Available on Azure AI Foundry
Last week, we kicked off the arrival of Meta’s powerful new Llama 4 models in Azure with the launch of three models across Azure AI Foundry and Azure Databricks. Today, we’re expanding the herd with the addition of two new 17B-parameter instruction-tuned models — now available in the Azure AI Foundry model catalog as Models as a Service (MaaS) endpoints New Models Llama-4-Scout-17B-16E-Instruct A fast, low-latency 17B model with 16 experts — optimized for general-purpose tasks with strong instruction following. Llama-4-Maverick-17B-128E-Instruct-FP8 A larger, more expressive variant with 128 experts and FP8 precision — built for heavier, higher-quality reasoning under constrained compute. Both models are: Hosted as serverless MaaS endpoints — no infrastructure setup required Available on GitHub Models and playground What Makes These Llama 4 Models Special? These models are part of Meta’s mixture-of-experts (MoE) family of Llama 4 variants. Unlike dense models, these MoE architectures selectively activate a subset of model parameters (experts) per token, yielding improved efficiency without sacrificing output quality. Scout-17B-16E offers fast inference for common enterprise workloads like summarization, Q&A, and structured output tasks. Maverick-17B-128E-FP8 introduces aggressive expert scaling and FP8 precision, enabling high-throughput inference with improved energy efficiency. How to Get Started You can find these models in the Azure AI Foundry model catalog — just search for "Llama-4" or navigate to the Meta model family. With a few clicks, you can: Deploy the model as a serverless endpoint Invoke it via the Azure AI Foundry playground Integrate using the Azure OpenAI-compatible REST API or Python SDK Use Cases These 17B models are a great fit for: Knowledge assistant copilots Long-form summarization Table-to-text transformation Conversational agents Internal developer tools Explore More To learn more about last week’s launch of Llama 4 models, including 8B and 70B variants, check out the official Azure blog: Introducing the Llama 4 Herd in Azure AI Foundry and Azure Databricks Try these models today in Azure AI Foundry and let us know what you build!Automated Document Validation That Auditors Trust: The Deterministic Advantage
The Hidden Challenge: Matching Fields Across Systems The core issue isn't AI's ability to extract information – modern systems like Azure Document Intelligence and GPT-4 Vision (or Qwen-2-VL) can identify and extract fields from documents with impressive accuracy. The real challenge comes afterward: how do you reliably match these extracted fields with your existing data? Consider a typical invoice processing scenario: AI extracts "Invoice Number: INV-12345" with 95% confidence Your ERP system shows "Invoice #: INV-12345" AI extracts "Issue Date: 01/02/2023" with 85% confidence Your ERP shows "Invoice Date: 1/2/2023" AI extracts "Amount: $1,500.00" with 92% confidence Your ERP shows "Total Due: $1,500" While humans can instantly see these are matches despite the different labels and formats, automated systems typically struggle. Most solutions either match everything (creating false positives) or are too restrictive (creating excessive manual reviews). Why Common Approaches Fall Short? Before diving into our solution, let's understand why popular matching techniques often disappoint in real-world scenarios. Many organizations start with fuzzy matching – essentially setting thresholds for how similar strings need to be before they're considered a match. It seems intuitive: if "Invoice Number" is 85% similar to "Invoice #", they must be the same field. But in practice, fuzzy matching introduces critical problems: Inconsistent thresholds: Set the threshold too high, and valid matches get missed (like "Invoice Date" vs. "Date of Invoice"). Set it too low, and you get false matches (like "Shipping Address" incorrectly matching with "Billing Address"). Field-by-field myopia: Fuzzy matching looks at each field in isolation rather than considering the document holistically. This leads to scenarios where Field A might match both Field X and Field Y with similar scores – with no way to determine which is correct without looking at all fields together. Format blindness: Standard fuzzy matching struggles with structural differences. A date formatted as "01/02/2023" vs. "2023-01-02" might look completely different character-by-character despite being identical semantically. One customer tried fuzzy matching for loan documents and found they needed to maintain over 300 different rules just to handle the variations in how dates were formatted across their systems! With the rapid advancement of large language models (LLMs) multi-modal capabilities, some organizations are tempted to simply feed their document fields into models like GPT-4 and ask, "Do these match?" While LLMs demonstrate impressive capability to understand context and variations, they introduce their own set of problems for business-critical document processing: Non-deterministic outputs: Ask an LLM the same question twice, and you might get different answers. For auditable business processes, this variability is unacceptable. The black box problem: When an LLM decides two fields match, can you explain exactly why? This lack of transparency becomes problematic for regulated industries requiring clear audit trails. Latency and cost issues: Running every field comparison through an LLM API adds significant time and expense, especially at scale. Hallucination risks: LLMs occasionally "make up" connections between fields that don't actually exist, potentially introducing critical errors in financial documents. One customer experimenting with LLM-based matching found that while accuracy seemed high in testing, the system occasionally matched names incorrectly due to contextual misunderstandings – a potentially grave issue for them. These approaches aren't entirely without merit – they're simply insufficient on their own for critical business processes requiring consistent, explainable, and globally optimal field matching. Beyond Rules-Based Matching: The Need for Intelligent Determinism Many organizations attempt to solve this with rules-based approaches: Exact matching: Requires perfect alignment (misses many valid matches). Keyword matching: Prone to false positives. Manual process flows: Time-consuming to build and maintain. Machine learning: Often inconsistent and unpredictable. What businesses truly need is a solution that combines the intelligence of AI with the reliability of deterministic processing – something that produces consistent, trustworthy results while handling real-world variations. After working with dozens of customers facing this exact problem, we developed a hybrid approach that bridges the gap between AI extraction and system validation. The key insight was that by applying mathematical optimization techniques (the same ones used in logistics for route planning), we could create a matching system that: Takes extracted document fields and reference data as inputs Computes a comprehensive similarity matrix accounting for: Text similarity (allowing for minor variations) Field name alignment (accounting for different naming conventions) Position information (where applicable) Confidence scores (from the AI extraction) Applies deterministic matching algorithms that guarantee: The same inputs always produce the same outputs Optimal matching based on global considerations, not just field-by-field rules Appropriate confidence thresholds that know when to escalate to humans Produces clear results that flag: Confirmed matches (for straight-through processing) Fields requiring review (with reasons why) Missing information The critical difference? Unlike black-box AI approaches, this system is fully deterministic. Given the same inputs, it always produces identical outputs – a must-have for regulated industries and audit requirements. Smart Warnings: Catching Issues Before They Become Problems One of the most powerful aspects of our solution is its proactive warning system. Unlike traditional approaches that either silently make incorrect matches or simply fail, our system identifies potential issues early in the process. How Our Warning System Works We built specific intelligence into the matching algorithm to detect suspicious patterns that might indicate a problem: for field_key, match in matches. Items(): if match["similarity"] < 0.2: logger. Warning(f"Field '{field_key}' has extremely low similarity ({match['similarity']:.2f}). Possibly missing in Document Intelligence or Doc JSON.") if contains_digit(match["field_value"]) and not contains_digit(match["candidate_text"]): logger. Warning(f"Field '{field_key}' appears to be numeric but the candidate text '{match['candidate_text']}' may be missing numeric information.") In plain English, this means: Unusually Low Similarity Detection: The system identifies when a match has been made with very low confidence (below 20%). This often indicates a field that's missing in one of the systems or a fundamental mismatch that needs human attention. Numeric Value Preservation Check: The system specifically watches for cases where a numeric field (like an amount, date, or account number) is matched with text that doesn't contain any numbers – a common error in document processing that can have serious consequences. Pattern-Based Warnings: Beyond these examples, the system includes specialized warnings for domain-specific issues, like date format mismatches or address component inconsistencies. Real World Outputs When processing financing documents, our system generates critical alerts like these: WARNING - Field 'vehicle_information.vehicle_identification_number' appears to be numeric but the candidate text 'Vehicle Identification Number' may be missing numeric information. WARNING - Field 'finance_information.annual_percentage_rate' appears to be numeric but the candidate text 'ANNUAL PERCENTAGE The cost of RATE your credit as a yearly rate.' may be missing numeric information. WARNING - Field 'itemization_of_amount_financed.total_downpayment.trade_in.equals_net_trade_in' appears to be numeric but the candidate text 'Net Trade In' may be missing numeric information. These warnings immediately highlight potentially serious issues in auto loan processing. In each case, the system detected that a critical numeric value (VIN number, interest rate, and trade-in amount) was matched with descriptive text rather than the actual numeric value. Without these alerts, a financing document could be processed with missing interest rates or incorrect vehicle identification, leading to compliance issues or financial discrepancies. This combination of deterministic matching with intelligent warnings transformed what was previously a multi-day correction process into an immediate fix at the point of document ingestion. The Business Impact of Early Warnings This warning system transformed how our customers handle document exceptions: For a mortgage processor, the numeric value check alone prevented dozens of potentially serious errors each week. In one case, it flagged a loan amount that had been incorrectly matched to a text field, potentially preventing a $250,000 discrepancy. More importantly, the warnings are generated in real-time during processing – not discovered weeks later during an audit or reconciliation. This means issues can be addressed immediately, often before they affect downstream business processes. The system also prioritizes warnings by severity, allowing operations teams to focus on the most critical issues first while letting minor variations through the process. Real-World Impact: From Hours to Seconds Let me share how this solution transformed operations for our customer. Before Implementation: 15-20 minutes per document for manual validation 30% of AI-extracted documents returned to manual processing 4 FTEs dedicated solely to validation and exception handling Frequent errors and inconsistencies across reviewers After Implementation: Validation time reduced to seconds per document Only 8% of documents now require human review 80% reduction in validation staff needed Consistent, auditable outputs with error rates below 0.5% The most significant improvement wasn't just in cost savings – it was in reliability. By implementing deterministic AI matching, the system could confidently process most documents autonomously while intelligently escalating only those requiring human attention. How It Works: A Practical Example Let's walk through a simple but illustrative example of how this works in practice: Imagine processing a batch of mortgage applications where an AI extraction system has identified key fields like applicant name, loan amount, property address, and income. These need to be matched against your existing CRM data. Traditional approaches would typically: Attempt exact matches on key identifiers Fail when formats differ slightly (e.g., "John A. Smith" vs. "John Smith") Require extensive rules for each field type Break when document layouts change Our deterministic AI matching approach: Creates a cost matrix measuring the similarity between each extracted field and potential CRM matches. Applies the Hungarian algorithm or Gale-Shapley (Stable Marriage) (from a Noble Prize Winning author's matching technique) to find the optimal assignments. We can use other algorithms as well. There are others as well which I highlighted on my blog. Uses confidence scores to identify uncertain matches. Produces a consistent, verifiable result every time. The practical outcome? What previously required a 45-minute manual review process now happens in seconds with higher accuracy. Mismatches that required human judgment (like slight name variations or formatting differences) are now handled automatically with mathematical precision. A key differentiator of our approach is thinking holistically about all fields together, rather than matching each field in isolation: Imagine an invoice with fields: Field A: "Invoice Number: INV-12345" Field B: "Date: 01/02/2023" Field C: "Total: $1,500.00" And your system data has: Field X: "Invoice #: INV-12345" Field Y: "Invoice Date: 1/2/2023" Field Z: "Total Due: $1,500" Traditional fuzzy matching might compare: A vs. X (90% match) A vs. Y (30% match) A vs. Z (25% match) B vs. X (30% match) And so on... It then makes individual decisions about each comparison, potentially matching fields incorrectly if they have similar scores. Our deterministic approach instead looks at the entire set of possibilities and finds the globally optimal arrangement that maximizes overall matching quality. It recognizes that while A could potentially match X, Y, or Z in isolation, the best overall solution is A→X, B→Y, C→Z. This holistic approach prevents errors that are common in field-by-field matching systems and produces more reliable results – particularly important when documents have many similar fields (like multiple date fields or address components). Beyond Financial Services: Applications Across Industries While our initial focus was financial services, we've seen this approach deliver similar value across industries: Healthcare Matching patient records across systems Reconciling insurance claims with provider documentation Validating clinical documentation against billing codes Manufacturing Aligning purchase orders with invoices and delivery notes Matching quality inspection reports with specifications Reconciling inventory records with physical counts Legal Services Comparing contract versions for discrepancies Matching clauses against legal libraries Validating discovery documents against case records Government Aligning citizen records across departments Validating grant applications against reference data Reconciling regulatory filings with internal systems The common thread? In each case, the solution bridges the gap between AI-extracted information and existing data systems, dramatically reducing the human validation burden. Implementation Insights: Lessons from the Field Throughout our implementation journey, we've learned several key lessons worth sharing: Start with the right foundation: The quality of your AI extraction matters enormously. Invest in high-quality document intelligence solutions like Azure Document Intelligence or similar tools that provide not just extracted text but confidence scores and spatial information. Tune your thresholds carefully: Every organization has different risk tolerances. Some prefer to review more documents manually to ensure zero errors; others prioritize throughput. The beauty of our approach is that these thresholds can be adjusted with precision – there's no need to rebuild models. Integrate human feedback loops: When human reviewers correct matches, capture that information to improve future matching. This doesn't require model retraining – simply adjusting cost functions and thresholds can continuously improve performance. Measure what matters: Don't just track error rates – measure business outcomes like processing time, exception rates, and staff productivity. One customer found that while their "match accuracy" only improved from 92% to 96%, their total processing time decreased by 85% because they eliminated review steps for high-confidence matches. Focus on explainability: Business users need to understand why matches were made (or flagged for review). Our system provides clear explanations that reference the specific elements that influenced each decision. The ROI Beyond Direct Savings While cost reduction is the most immediate benefit, our customers have discovered several additional advantages: Scalability Without Proportional Headcount: As document volumes grow, the system scales linearly without requiring additional reviewers. One customer increased their document processing volume by 300% while adding just one reviewer to their team. Improved Compliance and Audit Readiness: Because the matching process is deterministic and documented, auditors can clearly see the logic behind each decision. This has helped several customers significantly reduce their audit preparation time. Enhanced Customer Experience: Faster document processing means quicker responses to customers. One lending customer reduced their application processing time from 5 days to under 48 hours, giving them a significant competitive advantage. Workforce Transformation: By eliminating tedious validation work, employees can focus on higher-value tasks that require human judgment. One customer repurposed their document review team to focus on unusual cases and process improvement, resulting in additional efficiency gains. Looking Forward: The Future of Document Processing Where do we go from here? The technology continues to evolve, but the core principles remain sound. Our roadmap includes: Enhanced Multi-Document Correlation: Matching fields not just against reference data but across multiple related documents (e.g., matching an invoice against its purchase order, packing slip, and receipt). Adaptive Thresholding: Dynamically adjusting confidence thresholds based on document types, field importance, and historical accuracy. Specialized Domain Models: Customized functions for specific industries and document types to further improve matching accuracy. Taking the Next Step: Is This Right for Your Organization? You might be wondering whether this approach could benefit your organization. Consider these questions: Are you currently using AI document extraction but still requiring significant manual validation? Do you process more than 1,000 documents monthly with structured data that needs to be matched or validated? Is your organization spending more than 20+ hours weekly on document validation activities? Would consistency and auditability in your document processing provide significant value? If you answered yes to two or more of these questions, you likely have an opportunity to transform your document processing approach. Conclusion The promise of fully automated document processing has remained elusive for many organizations – not because of limitations in extracting information, but because of the challenge in reliably matching that information with existing systems. By combining the power of AI extraction with the reliability of deterministic matching algorithms, we've helped organizations bridge this critical gap. The results speak for themselves: dramatically reduced processing times, significant cost savings, improved accuracy, and enhanced scalability. In an era where every efficiency matters, solving the document matching challenge represents one of the highest-ROI investments an organization can make in its digital transformation journey. I'd love to hear about your document processing challenges and experiences. Have you found effective ways to match extracted document data with your systems? What approaches have worked well for your organization? Share your thoughts in the comments! If you're interested in learning more about deterministic AI matching or discussing how it might apply to your document processing challenges, feel free to connect or message me directly. #DocumentIntelligence #AIAutomation #DigitalTransformation #ProductivityGains #DocumentProcessing #DeterministicAgent References: Technical Reading: Mastering Document Field Matching: A complete (?) guide Code: setuc/Matching-Algorithms: A comprehensive collection of field matching algorithms for document data extraction. This repository includes implementations of Hungarian, Greedy, Gale-Shapley, ILP-based, and Approximate Nearest Neighbor algorithms along with synthetic data generation, evaluation metrics, and visualization tools. Note: The code above is not the actual solution that I described earlier but does have the core algorithms that we have used. You should be able to adapt them for your needs.AI Avatars: Redefining Human-Digital Interaction in the Enterprise Era
In today’s AI-driven world, businesses are constantly seeking innovative ways to humanize digital experiences. AI Avatars are emerging as a powerful solution—bridging the gap between intelligent automation and authentic, human-like engagement. With advancements in speech synthesis, large language models, and avatar rendering technologies, organizations can now deploy AI-powered digital assistants that not only understand and respond but also interact with a lifelike presence. The Rise of AI Avatars in Enterprise Applications AI Avatars go beyond traditional chatbots or voice assistants. These virtual beings offer multimodal interaction—combining voice, visual cues, and conversational intelligence into a seamless user experience. Built on enterprise-grade platforms like Azure AI, these avatars can be integrated into customer support portals, digital kiosks, internal knowledge hubs, and more. Their utility spans a range of industries: Retail: Personalized shopping assistants that guide consumers through products. Healthcare: Virtual health concierges that help patients navigate care. Education: Interactive tutors that deliver lessons with empathy and responsiveness. HR and Training: Onboarding avatars that answer employee questions, onboard new hires, or provide compliance updates. One of our key partners, Cloudforce, has integrated AI Avatar technology directly into their flagship platform nebulaONE®. This integration enables enterprises to deploy digital assistants that are deeply embedded in business processes, offering contextualized support and real-time engagement. From training and onboarding to employee self-service, nebulaONE's agentic AI Avatars act as a digital bridge between users and systems—driving efficiency, engagement, and satisfaction. Partner Spotlight: Cloudforce’s Avatar Initiative To operationalize and productize AI Avatars, Microsoft collaborates with a growing ecosystem of partners. Cloudforce is one of the early pioneers in this space. Their work in embedding avatars into nebulaONE demonstrates what’s possible when advanced AI meets real-world enterprise needs. With a vision to transform user interaction across industries, Cloudforce built a production-grade AI Avatar module designed to support customer Q&A, knowledge discovery, and live guided walkthroughs. Leveraging Azure OpenAI, Azure AI Speech, and privately-deployed secure cloud infrastructure, they have brought conversational intelligence to life—with both a face and a voice. Looking ahead, Cloudforce’s broader vision is to bring AI Avatar capabilities to millions of students—delivering immersive learning experiences that blend interactivity, personalization, and scale. Their education-focused roadmap enhancements highlight the potential of avatars not just as productivity agents, but as accessible and empathetic digital educators, delivering equitable access to knowledge previously reserved for a fortunate few. This kind of partner innovation illustrates how AI Avatars can be customized and scaled to deliver tangible business value across multiple domains. Partner Contribution "Students are already embracing generative AI at a pace and proficiency that far exceeds many professional audiences. With Azure's AI Avatar technology, educators and institutions can tailor unique GenAI interactions that promote reasoning and learning over simply receiving answers the way they would with common public bots." says Husein Sharaf, Founder and CEO at Cloudforce. "We understand the concerns and hesitation that our education partners are currently grappling with, however we believe they can and should take an active role in shaping how this transformative technology is leveraged across their campuses, or risk being left behind as students choose their own adventure." "Microsoft's enterprise AI capabilities are enabling partners like us to deliver secure, cost-efficient, and responsible AI experiences at scale. With the Azure AI Foundry and key innovations like AI Avatars as our building blocks, the nebulaONE platform is poised to serve as the GenAI gateway to tens of thousands of business users, and millions of students at leading educational institutions globally. Our customers are seeking unique differentiators that will enable them to compete and win in the age of AI, and our collaboration with Microsoft is empowering us to deliver just that." Summary AI Avatars represent the next frontier in digital interaction. By combining conversational AI, expressive voice synthesis, and realistic visual rendering, these intelligent agents deliver truly human-like experiences—at scale. They are not just tools, but digital extensions of your brand. Partners like Cloudforce are leading the way with innovative platforms like nebulaONE, showing how this technology can be embedded into enterprise solutions and educational experiences to drive efficiency with a human touch. While Cloudforce is among the first to productize AI Avatars using Azure AI, they are part of a growing movement—helping to shape the future of AI-powered experiences across industries. As AI continues to evolve, avatars will become a standard interface—transforming the way we learn, work, and engage with digital systems.
