How MS Discovery Is Empowering Scientists to Do More
Research and development has traditionally been a slow, sequential, and largely manual endeavour. Scientists formulate hypotheses, design experiments, run computations in constrained environments, and document results, each stage dependent on the last, each transition requiring human review and intervention. Knowledge is fragmented across systems, insights are bottlenecked by individual capacity, and the gap between hypothesis and actionable outcome can span weeks or months. For organisations tackling complex scientific and operational challenges, from drug discovery to industrial process optimisation, this pace of iteration is simply no longer acceptable. At Microsoft, we recently introduced Microsoft Discovery, a platform that I believe fundamentally changes this model. Much like Microsoft 365 transformed the way knowledge workers collaborate and create, Microsoft Discovery is designed to simplify and empower the way scientists and researchers work. It provides a unified, end-to-end platform that integrates advanced artificial intelligence, high-performance computing, and knowledge management to support the full scientific reasoning lifecycle: knowledge gathering, hypothesis generation, experiment design, simulation, results analysis, and documentation. In this article, I want to share how we used Microsoft Discovery to automate a real-world simulation workflow for a mining organisation and what that experience taught our team about the future of AI-augmented science. What Is Microsoft Discovery? Microsoft Discovery is Microsoft's scientific AI platform, a solution designed to accelerate research and experimentation across the full innovation lifecycle. Rather than replacing scientific judgement, Discovery is designed to amplify human expertise, embedding AI assistance at each stage of the R&D process while maintaining governance, traceability, and scientific rigour. From Traditional R&D to AI-Augmented Science To appreciate what Discovery enables, it is important to understand where it fits in. In the traditional R&D model, knowledge discovery centres on manual literature reviews and historical data analysis. Researchers individually search, read, and synthesise information which is a time-intensive process where discovery is limited by each person's capacity to locate and interpret relevant material. Hypothesis generation and experimental design are expert-led and largely manual. Computational experimentation, where it exists, runs in fixed or constrained environments with limited parallelism. Analysis and iteration follow the same sequential pattern: execute, review, document, repeat. Microsoft Discovery changes this fundamentally. In the AI-cloud-enabled model it provides: Knowledge synthesis at scale — Researchers can explore literature, historical experiments, and organisational knowledge through a single interface, with intelligent indexing surfacing insights faster than manual search could ever achieve. AI-assisted hypothesis generation — Collaborative human-and-AI workflows support hypothesis exploration and feasibility assessment, while final decisions remain with the scientist. Cloud-scale experimentation — Elastic compute and parallel processing allow simulations and experiments to run at scale, with integrated tracking and reproducibility built in. Continuous feedback and human-in-the-loop governance — Results are analysed and compared more rapidly, enabling faster iteration, with AI-generated insights reviewed and validated by researchers before action. Governed knowledge assets — Experiment lineage, outcomes, and best practices are captured as reusable, governed assets, supporting long-term organisational learning. The net effect is a transition from slow, manual, and fragmented research processes to an agile, automated, and data-driven R&D model — one that improves research efficiency, increases the return on innovation investment, and enables faster, higher-impact solutions to complex challenges. In high level, the research and deveolopment loop we discussed and how Microsoft Discovery enriches it show in the following diagram. The Real-World Problem: Screening Thousands of Molecules To bring this to life, let me walk you through a real-world use case we worked on recently. A mining organisation needed to identify the best-performing oxidant compounds for a chemical reaction central to their operations. We will be talking about only a workflow that sits squarely in the simulation phase of the scientific loop — and it is a perfect example of the kind of work that Microsoft Discovery can strongly transform. How Scientists Did It Before In the traditional process, scientists would begin by selecting candidate molecules from established molecular libraries based on characteristics identified through literature review. These libraries can contain thousands of molecules, each defined in standard molecular file formats (such as XYZ or CIF files) that describe their three-dimensional atomic structures. From there, a researcher would manually work through a multi-step pipeline: Pre-processing and preparation: The selected molecular files are processed and prepared for quantum mechanical (QM) calculations. This involves filtering molecules based on properties like the types of metals present, electron count, and atomic weight — criteria that directly affect both the scientific relevance and the computational cost of the simulations. The output is a set of prepared input files (known as GJF files) ready for simulation. Running quantum mechanical simulations: The prepared input files are submitted to a computational chemistry tool (Gaussian 16) to perform Density Functional Theory (DFT) calculations. These simulations compute the electronic structure and energy states of each molecule across different charge and multiplicity configurations. Crucially, each molecule requires multiple independent simulation runs, and the computational cost scales rapidly with molecular complexity. With thousands of candidate molecules, this step alone can involve thousands of individual simulation jobs. Collecting and post-processing results: Once all simulations complete, the output log files are collected and processed. For each molecule, the lowest-energy charge and multiplicity combination is identified, and a set of quantum mechanical descriptors and classical molecular descriptors are extracted. These descriptors are then fed into a trained machine learning model to predict the redox potential of each compound, a key metric that indicates how effectively a molecule can act as an oxidant in the target reaction. Summarisation and filtering: Finally, the predicted redox potentials and other relevant characteristics are compiled into a summary, enabling researchers to identify the most promising candidates for further investigation and experimental validation. Every step in this pipeline required manual intervention: writing and adjusting scripts, verifying input and output files, monitoring job queues, handling failures, and stitching results together. A single researcher could easily spend days or weeks moving through this process — and any error at one stage meant going back and re-running subsequent steps. How We Automated This with Microsoft Discovery Agents When we looked at this workflow through the lens of Microsoft Discovery, the opportunity was clear. The scientific reasoning, selecting which molecules to test, interpreting redox potential results, deciding what to investigate next, should remain with the researcher. But the operational overhead of preparing files, submitting simulations, monitoring jobs, collecting results, and assembling summaries? That could be orchestrated by a team of AI agents. A Team of Agents, Working Together We designed a multi-agent architecture within Microsoft Discovery to automate this simulation workflow end to end. Here is how the team of agents operates: Router Agent: The entry point. When a researcher submits a request for example, asking to run QM calculations on a set of candidate molecules the Router Agent interprets the intent and orchestrates the downstream workflow. Planner Agent: Once the Router Agent identifies the task, the Planner Agent examines the input files provided by the researcher and formulates a step-by-step execution plan. It determines what needs to happen, in what order, and with what parameters, much like a project manager scoping out a piece of work. Gaussian Prep Agent: This agent handles the preparation step. It is intelligent enough to inspect the current molecular files, apply the necessary filtering criteria, and prepare them for simulation, generating the input files that the computational chemistry tool requires. What previously involved manual scripting and file-by-file verification is now handled autonomously. We used Microsoft Discovery tools to do the underlying execution with this agent. MPI Gaussian Agent: This is where the power of cloud-scale computing comes in. The Gaussian Agent submits the prepared simulation jobs and manages their execution using an MPI-based master-worker pattern. This approach enables massive parallel execution scaling out across the cloud to run thousands of simulations concurrently rather than sequentially. Given that the candidate molecule libraries can contain thousands of entries, and each molecule may require multiple simulation runs, this parallel execution capability is transformative. What might have taken days in a constrained local environment can now complete in a fraction of the time. Redox Potential Agent: Once the simulations are complete, this agent takes over. It processes the simulation outputs, identifies the optimal charge and multiplicity state for each molecule, extracts the relevant QM and classical descriptors, and runs them through the trained machine learning model to predict redox potentials. Summariser Agent: The final agent in the chain. It maps the predicted redox potentials back to the original molecules, applies any additional filtering criteria, and produces a clean, structured summary a JSON file that the researcher can immediately use to identify the most promising candidates and take them forward into the next phase of their work. What the Researcher Experiences From the scientist's perspective, the transformation is striking. Instead of spending days writing scripts, babysitting job queues, and manually stitching results together, they provide their input files and describe what they need. The agents take it from there planning, preparing, executing, processing, and summarising and deliver a curated output ready for scientific interpretation. The researcher's time is freed to focus on what matters most: thinking critically about the science. Which molecules look most promising? What does the redox potential distribution tell us? Should we adjust the filtering criteria and run another round? These are the high-value questions that require human expertise and now scientists can spend their time on exactly that, rather than on operational mechanics. The Bigger Picture: Accelerating the Entire Scientific Loop It is important to note that this simulation workflow is just one piece of the broader scientific loop. The full cycle of scientific research, from initial knowledge gathering and literature review, through hypothesis generation, experimental design, simulation, results analysis, and documentation involves many stages, each of which can benefit from the same kind of AI-augmented approach. Microsoft Discovery is designed to support this entire cycle. In our project, we did not stop at simulation. We also explored how agents can accelerate the knowledge gathering phase, helping researchers navigate vast bodies of literature and surface relevant prior work more efficiently. We looked at how AI can assist with hypothesis generation and evaluation, helping scientists reason about which directions are most promising before committing to expensive computations. And we examined how agents can support the analysis and reporting phases comparing results against hypotheses, generating visualisations, and even assisting with drafting research documents. What excites me most about Microsoft Discovery is not any single capability, but the cumulative effect of embedding AI assistance across every stage of the research process. Each phase that gets faster and more efficient creates a multiplier effect on the phases that follow. When knowledge gathering takes hours instead of weeks, researchers generate better hypotheses sooner. When simulations run at cloud scale in parallel, results arrive faster. When analysis is augmented by AI, iteration cycles tighten. The entire loop accelerates. Conclusion The way we approach scientific research is undergoing a fundamental shift. Large language models and the AI agents built from them are not replacing scientists, they are empowering them to work at a pace and scale that was previously unimaginable. Microsoft Discovery represents a new operating model for R&D. By combining advanced AI, high-performance cloud computing, and intelligent workflow orchestration, it enables researchers to offload the repetitive, time-consuming operational work to agents and invest their expertise where it has the greatest impact: in asking better questions, interpreting complex results, and pushing the boundaries of what we know. In the use case I have shared here, a team of six AI agents automated a simulation pipeline that would have taken a single researcher days of manual work. They prepared molecular input files, scaled out thousands of quantum mechanical simulations in parallel across the cloud, processed the results, predicted redox potentials using machine learning, and delivered a structured summary all with minimal human intervention. This is just the beginning. As AI agents become more capable and the tools surrounding them more mature, the potential to accelerate discovery across every scientific domain is immense. Whether you are in materials science, pharmaceuticals, energy, agriculture, or any field where complex R&D is central to progress, Microsoft Discovery offers a platform to do more, faster, and with greater confidence. The future of science is not about working harder. It is about working smarter with AI as your partner in discovery.
Transforming Video Content into Structured SOPs Using Graph-based RAG
Introduction In today’s digital-first environments, a large portion of enterprise knowledge lives inside video content, training sessions, onboarding walkthroughs, and recorded operational procedures. While videos are great for learning, they are not ideal for quick reference, compliance, or repeatable processes. Converting that knowledge into structured documentation like Standard Operating Procedures (SOPs) is often manual and time-consuming. What if this process could be automated using AI? The Problem Transcripts alone don’t solve the problem. When videos are converted into text, the output typically lacks: Clear structure (sections, headings, hierarchy) Context (relationships between steps, tools, and roles) Completeness (definitions and dependencies spread across the content) This leads to a common challenge: Teams spend significant effort manually reading transcripts, interpreting context, and restructuring them into usable documentation. As seen in modern architecture challenges, manual and repetitive configurations don’t scale well and increase maintenance effort Enter Graph-based RAG (GraphRAG) GraphRAG extends traditional RAG by building a knowledge graph instead of treating content as disconnected chunks. What GraphRAG Does Extracts entities (tools, systems, roles, concepts) Maps relationships between them Groups related concepts into logical sections Preserves context across the entire document Architecture Overview Below is the high-level pipeline: Video → Transcription → Knowledge Graph → LLM Generation → Structured SOP Implementation Approach (Step-by-Step) Stage 1: Knowledge Graph Construction Convert video to transcript Split transcript into chunks Feed chunks into GraphRAG GraphRAG performs: Text Unit Extraction Entity Recognition Relationship Mapping Community Detection Result: A structured knowledge graph representation of the transcript Stage 2: Structure Extraction From the knowledge graph: Sequential Steps Preserve procedural flow from transcript order Logical Sections Derived using community detection Key Concepts Identified using graph centrality (importance via connections) This creates a framework for the SOP Stage 3: Intelligent Document Generation Using Azure OpenAI, each SOP section is generated: Section Generated From Title & Purpose High-level concepts Scope Entity boundaries Definitions Entity descriptions Responsibilities Role-based entities Procedures Sequential steps References Linked content The key advantage: LLM is grounded in graph structure not raw text Key Benefits Context Preservation - Relationships between concepts are maintained across sections. Comprehensive Coverage - Community detection ensures important topics are not missed. Reduced Hallucination - LLM generation is grounded in structured knowledge. Scalability- Works for: 30-minute tutorials, 3-hour training sessions and Enterprise knowledge bases Real-World Impact (Example) In enterprise scenarios like pharmaceutical SOP generation: Processing time: ~15–20 minutes for a multi-hour video Output quality: 8–10 structured SOP sections Consistency: Terminology and relationships preserved Coverage: Minimal missing topics Where This Approach Works Best Training videos → SOPs Meeting recordings → action summaries Technical demos → documentation Interview recordings → knowledge bases Tutorials → reference guides Key Takeaway This approach represents a shift from text processing → knowledge understanding. By combining: Knowledge graphs (structure) LLMs (language generation) We can transform raw, unstructured content into usable, enterprise-grade knowledge assets. Resources https://microsoft.github.io/graphrag/index/overview/ Final Thoughts Have you explored GraphRAG or similar approaches in your projects? What challenges did you face? How did you handle unstructured knowledge? Share your experiences — let’s learn together.
Centralizing Enterprise API Access for Agent-Based Architectures
Problem Statement When building AI agents or automation solutions, calling enterprise APIs directly often means configuring individual HTTP actions within each agent for every API. While this works for simple scenarios, it quickly becomes repetitive and difficult to manage as complexity grows. The challenge becomes more pronounced when a single business domain exposes multiple APIs, or when the same APIs are consumed by multiple agents. This leads to duplicated configurations, higher maintenance effort, inconsistent behavior, and increased governance and security risks. A more scalable approach is to centralize and reuse API access. By grouping APIs by business domain using an API management layer, shaping those APIs through a Model Context Protocol (MCP) server, and exposing the MCP server as a standardized tool or connector, agents can consume business capabilities in a consistent, reusable, and governable manner. This pattern not only reduces duplication and configuration overhead but also enables stronger versioning, security controls, observability, and domain‑driven ownership—making agent-based systems easier to scale and operate in enterprise environments. Designing Agent‑Ready APIs with Azure API Management, an MCP Server, and Copilot Studio As enterprises increasingly adopt AI‑powered assistants and Copilots, API design must evolve to meet the needs of intelligent agents. Traditional APIs—often designed for user interfaces or backend integrations—can expose excessive data, lack intent-level abstraction, and increase security risk when consumed directly by AI systems. This document outlines a practical, enterprise-‑ready approach to organize APIs in Azure API Management (APIM), introduce a Model Context Protocol (MCP) server to shape and control context, and integrate the solution with Microsoft Copilot Studio. The goal is to make APIs truly agent-‑ready: secure, scalable, reusable, and easy to govern. Architecture at a glance Back-end services expose domain APIs. Azure API Management (APIM) groups and governs those APIs (products, policies, authentication, throttling, versions). An MCP server calls APIM, orchestrates/filters responses, and returns concise, model-friendly outputs. Copilot Studio connects to the MCP server and invokes a small set of predictable operations to satisfy user intents. Why Traditional API Designs Fall Short for AI Agents Enterprise APIs have historically been built around CRUD operations and service-‑to-‑service integration patterns. While this works well for deterministic applications, AI agents work best with intent-driven operations and context-aware responses. When agents consume traditional APIs directly, common issues include: overly verbose payloads, multiple calls to satisfy a single user intent, and insufficient guardrails for read vs. write operations. The result can be unpredictable agent behavior that is difficult to test, validate, and govern. Structuring APIs Effectively in Azure API Management Azure API Management (APIM) is the control plane between enterprise systems and AI agents. A well-‑structured APIM instance improves security, discoverability, and governance through products, policies, subscriptions, and analytics. Key design principles for agent consumption Organize APIs by business capability (for example, Customer, Orders, Billing) rather than technical layers. Expose agent-facing APIs via dedicated APIM products to enable controlled access, throttling, versioning, and independent lifecycle management. Prefer read-only operations where possible; scope write operations narrowly and protect them with explicit checks, approvals, and least-privilege identities. Read‑only APIs should be prioritized, while action‑oriented APIs must be carefully scoped and gated. The Role of the MCP Server in Agent‑Based Architectures APIM provides governance and security, but agents also need an intent-level interface and model-friendly responses. A Model Context Protocol (MCP) server fills this gap by acting as a mediator between Copilot Studio and APIM-exposed APIs. Instead of exposing many back-end endpoints directly to the agent, the MCP server can: orchestrate multiple API calls, filter irrelevant fields, enforce business rules, enrich results with additional context, and emit concise, predictable JSON outputs. This makes agent behavior more reliable and easier to validate. Instead of exposing multiple backend APIs directly to the agent, the MCP server aggregates responses, filters irrelevant data, enriches results with business context, and formats responses into LLM‑friendly schemas. By introducing this abstraction layer, Copilot interactions become simpler, safer, and more deterministic. The agent interacts with a small number of well‑defined MCP operations that encapsulate enterprise logic without exposing internal complexity. Designing an Effective MCP Server An MCP server should have a focused responsibility: shaping context for AI models. It should not replace core back-end services; it should adapt enterprise capabilities for agent consumption. What MCP should do An MCP server should be designed with a clear and focused responsibility: shaping context for AI models. Its primary role is not to replace backend services, but to adapt enterprise data for intelligent consumption. MCP does not orchestrate enterprise workflows or apply business logic. It standardizes how agents discover and invoke external tools and APIs by exposing them through a structured protocol interface. Orchestration, intent resolution, and policy-driven execution are handled by the agent runtime or host framework. It is equally important to understand what does not belong in MCP. Complex transactional workflows, long‑running processes, and UI‑specific formatting should remain in backend systems. Keeping MCP lightweight ensures scalability and easier maintenance. Call APIM-managed APIs and orchestrate multi-step retrieval when needed. Apply security checks and business rules consistently. Filter and minimize payloads (return only fields needed for the intent). Normalize and reshape responses into stable, predictable JSON schemas. Handle errors and edge cases with safe, descriptive messages. What MCP should not do Avoid implementing complex transactional workflows, long-running processes, or UI-specific formatting in MCP. Keep it lightweight so it remains scalable, testable, and easy to maintain. Step by step guide 1) Create an MCP server in Azure API Management (APIM) Open the Azure portal (portal.azure.com). Go to your API Management instance. In the left navigation, expand APIs. Create (or select) an API group for the business domain you want to expose (for example, Orders or Customers). Add the relevant APIs/operations to that API group. Create or select an APIM product dedicated for agent usage, and ensure the product requires a subscription (subscription key). Create an MCP server in APIM and map it to the API (or API group) you want to expose as MCP operations. In the MCP server settings, ensure Subscription key required is enabled. From the product’s Subscriptions page, copy the subscription key you will use in Copilot Studio. Screenshot placeholders: APIM API group, product configuration, MCP server mapping, subscription settings, subscription key location. * Note: Using an API Management subscription key to access MCP operations is one supported way to authenticate and consume enterprise APIs. However, this approach is best suited for initial setups, demos, or scenarios where key-based access is explicitly required. For production‑grade enterprise solutions, Microsoft recommends using managed identity–based access control. Managed identities for Azure resources eliminate the need to manage secrets such as subscription keys or client secrets, integrate natively with Microsoft Entra ID, and support fine‑grained role‑based access control (RBAC). This approach improves security posture while significantly reducing operational and governance overhead for agent and service‑to‑service integrations. Wherever possible, agents and MCP servers should authenticate using managed identities to ensure secure, scalable, and compliant access to enterprise APIs. 2) Create a Copilot Studio agent and connect to the APIM MCP server using a subscription key Copilot Studio natively supports Model Context Protocol (MCP) servers as tools. When an agent is connected to an MCP server, the tool metadata—including operation names, inputs, and outputs—is automatically discovered and kept in sync, reducing manual configuration and maintenance overhead. Sign in to Copilot Studio. Create a new agent and add clear instructions describing when to use the MCP tool and how to present results (for example, concise summaries plus key fields). Open Tools > Add tool > Model Context Protocol, then choose Create. Enter the MCP server details: Server endpoint URL: copy this from your MCP server in APIM. Authentication: select API Key. Header name: use the subscription key header required by your APIM configuration. Select Create new connection, paste the APIM subscription key, and save. Test the tool in the agent by prompting for a domain-specific task (for example, “Get order status for 12345”). Validate that responses are concise and that errors are handled safely. Screenshot placeholders: MCP tool creation screen, endpoint + auth configuration, connection creation, test prompt and response. Operational best practices and guardrails Least privilege by default: create separate APIM products and identities for agent scenarios; avoid broad access to internal APIs. Prefer intent-level operations: expose fewer, higher-level MCP operations instead of many low-level endpoints. Protect write operations: require explicit parameters, validation, and (when appropriate) approval flows; keep “read” and “write” tools separate. Stable schemas: return predictable JSON shapes and limit optional fields to reduce prompt brittleness. Observability: log MCP requests/responses (with sensitive fields redacted), monitor APIM analytics, and set alerts for failures and throttling. Versioning: version MCP operations and APIM APIs; deprecate safely. Security hygiene: treat subscription keys as secrets, rotate regularly, and avoid exposing them in prompts or logs. Summary As organizations scale agent‑based and Copilot‑driven solutions, directly exposing enterprise APIs to AI agents quickly becomes complex and risky. Centralizing API access through Azure API Management, shaping agent‑ready context via a Model Context Protocol (MCP) server, and consuming those capabilities through Copilot Studio establishes a clean and governable architecture. This pattern reduces duplication, enforces consistent security controls, and enables intent‑driven API consumption without exposing unnecessary backend complexity. By combining domain‑aligned API products, lightweight MCP operations, and least‑privilege identity‑based access, enterprises can confidently scale AI agents while maintaining strong governance, observability, and operational control. References Azure API Management (APIM) – Overview Azure API Management – Key Concepts Azure MCP Server Documentation (Model Context Protocol) Extend your agent with Model Context Protocol Managed identities for Azure resources – OverviewEnabling Agentic Data Governance with Hybrid Cloud Flexibility in Azure
The “Why” Do you manage data in a complex multi-cloud environment? Are you struggling with data silos, evolving regulations, and the pressure to maintain control and compliance across on-prem and multiple clouds? Do you ever wish an intelligent assistant could help shoulder the load of data governance? If so, I can relate. Let me tell you a story that might sound familiar. Meet Mark (pictured above). He is a data governance officer at Contoso (a fictional but very representative enterprise). Mark’s day job is ensuring data governance and compliance across his company’s vast hybrid cloud estate – think around ~2 million data assets sprawled across 12+ datacenters on-premises and in different public clouds. Regulatory requirements are constantly shifting. Customer data is increasingly sensitive. Each department and region has its own way of doing things. Mark is fighting an uphill battle with data silos and disconnected cloud operations. He bounces between a patchwork of tools – spreadsheets, cloud consoles, governance portals – trying to answer basic questions: Where is our data? Who’s using it? Are we in compliance? Armed with an old desk calculator and a pile of paper-based reports (a perfect 1990s backdrop), he is dealing with the data around him that has exploded in volume and complexity. What if Mark had a single pane of glass. The glass that reflects and acts. It reflects your governance state and enforces compliance – a self-hydrating pane of glass accompanied by a conversational AI. And he’s not alone. We’re all living in a data overload era. Every day, organizations generate and ingest more information than ever before. Transistors and mainframes gave way to the internet boom of the ’90s, then an explosion of mobile devices in the 2000s, social media in the 2010s, and now widespread cloud computing – all funneling data into our systems at an exponential rate. On top of that, a new wave of AI and conversational interfaces has arrived here in the mid-2020s, making data more accessible but also increasing expectations for real-time insight. It’s no wonder modern IT leaders feel overwhelmed. But these challenges are also opportunities. The way I see it, the incredible growth of data and cloud capabilities means we have a chance to reimagine data governance. The fact that I’m writing about this right now is no coincidence. My customers are looking to resolve problems in this space. In my conversations with them, I hear the same needs: We want better governance, more visibility, streamlined oversight… and cherry on top, we want it in an “agentic” fashion. In other words, they want to delegate the grunt work to the platform toolset augmented by AI, so they can focus on higher-value tasks. The “What” That vision – agentic data governance with hybrid cloud flexibility – became the driver for this work. This is a modular solution, and you have these building block style components (cloud services, governance tools, AI agents), which you can snap them together into an intended solution. Think of it as a jumpstart kit for continuous data governance across multiple clouds, with autonomous (“agentic”) assistance baked in that you can leverage and build upon. It’s not the final, productized solution – more a vision of what’s possible. Contoso’s Requirements These are the high-level requirements from Contoso: Data governance across clouds under one roof A single pane of glass dashboard consolidating reporting on the 5 governance domains: o Visibility on data residency and lineage o PII (Personally Identifiable Information) must run on a CC (Confidential Compute) o Security software (Defender) compliance o Resource tagging compliance (foundational for a good governance posture) o OS updates compliance Ability to enforce compliance in an agentic manner with a human in the loop Agentic enforcement of compliance pertaining to residency and confidential compute Solution – The breakdown The solution is comprised of 8 modules addressing these requirements. These solution modules are: Foundational (Landing zones, Data Sources, Operational setup, Policies, etc.) Dashboard Hydration + Agentic Reporting – Residency Compliance Dashboard Hydration + Agentic Reporting – Confidential Compute for PII Compliance Dashboard Hydration + Agentic Reporting – MS Defender Compliance Dashboard Hydration + Agentic Reporting – Resource Tag Compliance Dashboard Hydration + Agentic Reporting – OS Updates/Patch Compliance Enforce Compliance via Copilot Agent - Residency Compliance Enforce Compliance via Copilot Agent – CC PII Compliance Solution – The architecture view These are the main technical components that make up the solution architecture: Data sources of all shapes and sizes on the left, governed by the native Azure or the Arc plane. Additional Azure services across the bottom layer for the foundational governance posture Microsoft Purview, in the top middle, as the unified data governance platform Microsoft Fabric, in the bottom middle, as the end-to-end ingestion and analytics platform Microsoft Power Platform, on the right, as the low code/no code business flow and the copilot agent experience Solution – The end user view So how does Mark see this solution as a data governance officer? He doesn’t see all the intricacies of the solution integration and the logic execution. He sees two things: A Power BI dashboard running on Microsoft Fabric with A compliance dashboard with an overall score in each of the five compliance domains alongside scores for each of the data products across these domains Additional reporting views for more granular reporting Fabric-based pipeline that hydrates the underlying semantic models from various sources to keep the reports fresh and current A Copilot agent (in Teams) for both: Reporting on all compliance domains Enforcing in-scope compliance across selected domains The agent takes care of it - queries Fabric’s semantic model, calls Azure Function endpoints, updates Purview glossary terms, applies Azure tags, and sends Teams notifications. The “How” – Residency Compliance Let’s pick a few modules to walk through how these solution modules work together to give a cohesive agentic governance experience to Mark. It’s Monday morning, and Mark logs into the Contoso governance portal with a cup of coffee in hand. Instead of a dozen browser tabs, he has two main tools opened: the Data Governance Dashboard and the Contoso Governance Copilot agent. To address some inquiries that came as an assigned action to him, he interacted with the agent. During this interaction, not only did he validate if there were any residency missing in the unified data governance platform (Purview), but he was also able to address a mismatch between Purview and Azure resource, based on the designed principles. Here is the snippet of the chat: Now, under the hood, several components have worked on behalf of the agent in performing this governance checking and applying the necessary course of action: Even before Mark's conversation with the agent, an ongoing hydration process keeps the Fabric Power BI dashboard up to date. Dashboard Hydration + Agentic Reporting – Residency Compliance A Fabric notebook runs the residency scorecard code block through a pipeline. It reads two Lakehouse tables containing latest residency information from Purview and the approved region list Then, the notebook gets a Microsoft Entra bearer token Once acquired, the notebook then calls an Azure Function endpoint This endpoint, then searches for the Azure resources associated with the data products in Purview using an Azure resource tag. The notebook then compares the declared Purview residency with the approved region list and the associated resource’s region The notebook then calculates the final 0 / 25 / 50 / 75 / 100 residency compliance score and a reason. For example: A data product without an associated Azure resource gets a 0, while a data product whose residency in Purview is an approved region by Contoso, and also matches with the associated Azure resource, gets a 100. It then writes the results to the relevant residency compliance Lakehouse tables The dedicated compliance table then feeds to the semantic model for reporting The compliance Power BI dashboard is hydrated Enforce Compliance via Copilot Agent - Residency Compliance With the dashboard data regularly updated, the agent follows this logic, the updated reporting data, and the actions at its disposal, during the earlier conversation with Mark : Mark initiates the conversation with the agent The agent calls a Power Automate flow This flow retrieves Purview’s residency information stored in the Fabric semantic model 5, 6, 7 and 8. When Mark asks to investigate further on a data product, the agent carries the conversation using a topic, which then leverages a flow, which uses a Power Automate custom connector to access an Azure Function endpoint. This endpoint then retrieves latest glossary (residency) information about the data product in question, from Purview, and provides a preview back to the user 10, 11, 12, and 13. If the update criteria are met, and if there is no conflict, and with Mark’s blessings, the topic then calls another flow to access the Functions Purview Update endpoint, and make the glossary (residency) update in Purview for that data product The “How” – Confidential Compute for PII Compliance Dashboard Hydration + Agentic Reporting – Confidential Compute for PII Compliance The following snippet shows how Mark addresses the compliance risk with a critical data product (application), S/4 HANA, and performed the necessary compliance actions, such as tagging the associated resources and notifying the data product owners via Teams channel. The following diagram shows the under-the-hood hydration flow for confidential compute compliance: Enforce Compliance via Copilot Agent – CC PII Compliance Finally, the diagram below shows how Mark’s conversation flows through the main solution components: Outcome Stepping back, what did we accomplish for Mark and Contoso? We turned an onslaught of governance challenges into an opportunity to modernize how data is managed. This gave Mark: Centralized Visibility into data assets across the landscape through Purview and a unified dashboard Proactive compliance enabled with automated checks - controlled with Purview exports and Fabric pipeline schedules And compliance enforcement using an agent Hybrid Cloud Consistency. By using Azure Arc and a foundational data plane management setup Reduced Operational overhead with agentic reporting and compliance Though the solution is comprised of wide variety of components/services, it is built from standard building blocks and is relatively simple to implement. In total, the solution combined around a dozen Azure services and over 40 distinct components (from Purview catalogs to data pipelines, to custom functions and flows). You can choose to implement some or all the compliance domains. Or, better yet, build upon and create new domains and pave new paths. Wrap-up I believe many enterprises could take a similar journey. If you’re facing these issues, consider this an invitation to think differently about data governance. Start with the pieces you already have – your own building blocks of cloud services and data – and imagine what you could build. Chances are that a lot of the heavy lifting can be orchestrated with today’s technology. And with the rise of AI copilots, the dream of agentic data governance – where your policies are continuously enforced by smart agents – is no longer science fiction. It’s here, right now, waiting for you to take it for a spin. Next steps Watch the video narrative on SAP on Azure YouTube channel: Build it with the GitHub Repository: https://github.com/moazmirza/data-sov-and-hyb-cloud Comments/questions: Here, or @ LinkedIn /moazmirza Solution Selfies Azure Policy Compliance - Foundational Governance Posture Purview Data Product Catalog and Data Lineage Purview Governance Metadata à Fabric Lakehouse Fabric Semantic Model Additional Fabric Power BI Dashboard Copilot Studio Topic Flow Azure Function EndpointsAdvancing to Agentic AI with Azure NetApp Files VS Code Extension v1.2.0
The Azure NetApp Files VS Code Extension v1.2.0 introduces a major leap toward agentic, AI‑informed cloud operations with the debut of the autonomous Volume Scanner. Moving beyond traditional assistive AI, this release enables intelligent infrastructure analysis that can detect configuration risks, recommend remediations, and execute approved changes under user governance. Complemented by an expanded natural language interface, developers can now manage, optimize, and troubleshoot Azure NetApp Files resources through conversational commands - from performance monitoring to cross‑region replication, backup orchestration, and ARM template generation. Version 1.2.0 establishes the foundation for a multi‑agent system built to reduce operational toil and accelerate a shift toward self-managing enterprise storage in the cloud.How Azure NetApp Files Object REST API powers Azure and ISV Data and AI services – on YOUR data
This article introduces the Azure NetApp Files Object REST API, a transformative solution for enterprises seeking seamless, real-time integration between their data and Azure's advanced analytics and AI services. By enabling direct, secure access to enterprise data—without costly transfers or duplication—the Object REST API accelerates innovation, streamlines workflows, and enhances operational efficiency. With S3-compatible object storage support, it empowers organizations to make faster, data-driven decisions while maintaining compliance and data security. Discover how this new capability unlocks business potential and drives a new era of productivity in the cloud.Unlocking Advanced Data Analytics & AI with Azure NetApp Files object REST API
Azure NetApp Files object REST API enables object access to enterprise file data stored on Azure NetApp Files, without copying, moving, or restructuring that data. This capability allows analytics and AI platforms that expect object storage to work directly against existing NFS based datasets, while preserving Azure NetApp Files’ performance, security, and governance characteristics.
Building a Secure and Compliant Azure AI Landing Zone: Policy Framework & Best Practices
As organizations accelerate their AI adoption on Microsoft Azure, governance, compliance, and security become critical pillars for success. Deploying AI workloads without a structured compliance framework can expose enterprises to data privacy issues, misconfigurations, and regulatory risks. To address this challenge, the Azure AI Landing Zone provides a scalable and secure foundation — bringing together Azure Policy, Blueprints, and Infrastructure-as-Code (IaC) to ensure every resource aligns with organizational and regulatory standards. The Azure Policy & Compliance Framework acts as the governance backbone of this landing zone. It enforces consistency across environments by applying policy definitions, initiatives, and assignments that monitor and remediate non-compliant resources automatically. This blog will guide you through: 🧭 The architecture and layers of an AI Landing Zone 🧩 How Azure Policy as Code enables automated governance ⚙️ Steps to implement and deploy policies using IaC pipelines 📈 Visualizing compliance flows for AI-specific resources What is Azure AI Landing Zone (AI ALZ)? AI ALZ is a foundational architecture that integrates core Azure services (ML, OpenAI, Cognitive Services) with best practices in identity, networking, governance, and operations. To ensure consistency, security, and responsibility, a robust policy framework is essential. Policy & Compliance in AI ALZ Azure Policy helps enforce standards across subscriptions and resource groups. You define policies (single rules), group them into initiatives (policy sets), and assign them with certain scopes & exemptions. Compliance reporting helps surface noncompliant resources for mitigation. In AI workloads, some unique considerations: Sensitive data (PII, models) Model accountability, logging, audit trails Cost & performance from heavy compute usage Preview features and frequent updates Scope This framework covers: Azure Machine Learning (AML) Azure API Management Azure AI Foundry Azure App Service Azure Cognitive Services Azure OpenAI Azure Storage Accounts Azure Databases (SQL, Cosmos DB, MySQL, PostgreSQL) Azure Key Vault Azure Kubernetes Service Core Policy Categories 1. Networking & Access Control Restrict resource deployment to approved regions (e.g., Europe only). Enforce private link and private endpoint usage for all critical resources. Disable public network access for workspaces, storage, search, and key vaults. 2. Identity & Authentication Require user-assigned managed identities for resource access. Disable local authentication; enforce Microsoft Entra ID (Azure AD) authentication. 3. Data Protection Enforce encryption at rest with customer-managed keys (CMK). Restrict public access to storage accounts and databases. 4. Monitoring & Logging Deploy diagnostic settings to Log Analytics for all key resources. Ensure activity/resource logs are enabled and retained for at least one year. 5. Resource-Specific Guardrails Apply built-in and custom policy initiatives for OpenAI, Kubernetes, App Services, Databases, etc. A detailed list of all policies is bundled and attached at the end of this blog. Be sure to check it out for a ready-to-use Excel file—perfect for customer workshops—which includes policy type (Standalone/Initiative), origin (Built-in/Custom), and more. Implementation: Policy-as-Code using EPAC To turn policies from Excel/JSON into operational governance, Enterprise Policy as Code (EPAC) is a powerful tool. EPAC transforms policy artifacts into a desired state repository and handles deployment, lifecycle, versioning, and CI/CD automation. What is EPAC & Why Use It? EPAC is a set of PowerShell scripts / modules to deploy policy definitions, initiatives, assignments, role assignments, exemptions. Enterprise Policy As Code (EPAC) It supports CI/CD integration (GitHub Actions, Azure DevOps) so policy changes can be treated like code. It handles ordering, dependency resolution, and enforcement of a “desired state” — any policy resources not in your repo may be pruned (depending on configuration). It integrates with Azure Landing Zones (including governance baseline) out of the box. References & Further Reading EPAC GitHub Repository Advanced Azure Policy management - Microsoft Learn [Advanced A...Framework] How to deploy Azure policies the DevOps way [How to dep...- Rabobank]
Architecting an Azure AI Hub-and-Spoke Landing Zone for Multi-Tenant Enterprises
A large enterprise customer adopting AI at scale typically needs three non‑negotiables in its AI foundation: End‑to‑end tenant isolation across network, identity, compute, and data Secure, governed traffic flow from users to AI services Transparent chargeback/showback for shared AI and platform services At the same time, the platform must enable rapid onboarding of new tenants or applications and scale cleanly from proof‑of‑concept to production. This article proposes an Azure Landing Zone–aligned architecture using a Hub‑and‑Spoke model, where: The AI Hub centralizes shared services and governance AI Spokes host tenant‑dedicated AI resources Application logic and AI agents run on AKS The result is a secure, scalable, and operationally efficient enterprise AI foundation. 1. Architecture goals & design principles Goals Host application logic and AI agents on Azure Kubernetes Service (AKS) as custom deployments instead of using agents under Azure AI Foundry Enforce strong tenant isolation across all layers Support cross chargeback and cost attribution Adopt a Hub‑and‑Spoke model with clear separation of shared vs. tenant‑specific services Design principles (Azure Landing Zone aligned) Azure Landing Zone (ALZ) guidance emphasizes: Separation of platform and workload subscriptions Management groups and policy inheritance Centralized connectivity using hub‑and‑spoke networking Policy‑driven governance and automation For infrastructure as code, ALZ‑aligned deployments typically use Bicep or Terraform, increasingly leveraging Azure Verified Modules (AVM) for consistency and long‑term maintainability. 2. Subscription & management group model A practical enterprise layout looks like this: Tenant Root Management Group o Platform Management Group Connectivity subscription (Hub VNet, Firewall, DNS, ExpressRoute/VPN) Management subscription (Log Analytics, Monitor) Security subscription (Defender for Cloud, Sentinel if required) o AI Hub Management Group AI Hub subscription (shared AI and governance services) o AI Spokes Management Group One subscription per tenant, business unit, or regulated boundary This structure supports enterprise‑scale governance while allowing teams to operate independently within well‑defined guardrails. 3. Logical architecture — AI Hub vs. AI Spoke AI Hub (central/shared services) The AI Hub acts as the governed control plane for AI consumption: Ingress & edge security: Azure Application Gateway with WAF (or Front Door for global scenarios) Central egress control: Azure Firewall with forced tunneling API governance: Azure API Management (private/internal mode) Shared AI services: Azure OpenAI (shared deployments where appropriate), safety controls Monitoring & observability: Azure Monitor, Log Analytics, centralized dashboards Governance: Azure Policy, RBAC, naming and tagging standards All tenant traffic enters through the hub, ensuring consistent enforcement of security, identity, and usage policies. AI Spoke (tenant‑dedicated services) Each AI Spoke provides a tenant‑isolated data and execution plane: Tenant‑dedicated storage accounts and databases Vector stores and retrieval systems (Azure AI Search with isolated indexes or services) AKS runtime for tenant‑specific AI agents and backend services Tenant‑scoped keys, secrets, and identities 4. Logical architecture diagram (Hub vs. Spoke) 5. Network architecture — Hub and Spoke 6. Tenant onboarding & isolation strategy Tenant onboarding flow Tenant onboarding is automated using a landing zone vending model: Request new tenant or application Provision a spoke subscription and baseline policies Deploy spoke VNet and peer to hub Configure private DNS and firewall routes Deploy AKS tenancy and data services Register identities and API subscriptions Enable monitoring and cost attribution This approach enables consistent, repeatable onboarding with minimal manual effort. Isolation by design Network: Dedicated VNets, private endpoints, no public AI endpoints Identity: Microsoft Entra ID with tenant‑aware claims and conditional access Compute: AKS isolation using namespaces, node pools, or dedicated clusters Data: Per‑tenant storage, databases, and vector indexes 7. Identity & access management (Microsoft Entra ID) Key IAM practices include: Central Microsoft Entra ID tenant for authentication and authorization Application and workload identities using managed identities Tenant context enforced at API Management and propagated downstream Conditional Access and least‑privilege RBAC This ensures zero‑trust access while supporting both internal and partner scenarios. 8. Secure traffic flow (end‑to‑end) User accesses application via Application Gateway + WAF Traffic inspected and routed through Azure Firewall API Management validates identity, quotas, and tenant context AKS workloads invoke AI services over Private Link Responses return through the same governed path This pattern provides full auditability, threat protection, and policy enforcement. 9. AKS multitenancy options Model When to use Characteristics Namespace per tenant Default Cost‑efficient, logical isolation Dedicated node pools Medium isolation Reduced noisy‑neighbor risk Dedicated AKS cluster High compliance Maximum isolation, higher cost Enterprises typically adopt a tiered approach, choosing the isolation level per tenant based on regulatory and risk requirements. 10. Cost management & chargeback model Tagging strategy (mandatory) tenantId costCenter application environment owner Enforced via Azure Policy across all subscriptions. Chargeback approach Dedicated spoke resources: Direct attribution via subscription and tags Shared hub resources: Allocated using usage telemetry o API calls and token usage from API Management o CPU/memory usage from AKS namespaces Cost data is exported to Azure Cost Management and visualized using Power BI to support showback and chargeback. 11. Security controls checklist Private endpoints for AI services, storage, and search No public network access for sensitive services Azure Firewall for centralized egress and inspection WAF for OWASP protection Azure Policy for governance and compliance 12. Deployment & automation Foundation: Azure Landing Zone accelerators (Bicep or Terraform) Workloads: Modular IaC for hub and spokes AKS apps: GitOps (Flux or Argo CD) Observability: Policy‑driven diagnostics and centralized logging 13. Final thoughts This Azure AI Landing Zone design provides a repeatable, secure, and enterprise‑ready foundation for any large customer adopting AI at scale. By combining: Hub‑and‑Spoke networking AKS‑based AI agents Strong tenant isolation FinOps‑ready chargeback Azure Landing Zone best practices organizations can confidently move AI workloads from experimentation to production—without sacrificing security, governance, or cost transparency. Disclaimer: While the above article discusses hosting custom agents on AKS alongside customer-developed application logic, the following sections focus on a baseline deployment model with no customizations. This approach uses Azure AI Foundry, where models and agents are fully managed by Azure, with centrally governed LLMs(AI Hub) hosted in Azure AI Foundry and agents deployed in a spoke environment. 🚀 Get Started: Building a Secure & Scalable Azure AI Platform To help you accelerate your Azure AI journey, Microsoft and the community provide several reference architectures, solution accelerators, and best-practice guides. Together, these form a strong foundation for designing secure, governed, and cost-efficient GenAI and AI workloads at scale. Below is a recommended starting path. 1️⃣ AI Landing Zone (Foundation) Purpose: Establish a secure, enterprise-ready foundation for AI workloads. The AI Landing Zone extends the standard Azure Landing Zone with AI-specific considerations such as: Network isolation and hub-spoke design Identity and access control for AI services Secure connectivity to data sources Alignment with enterprise governance and compliance 🔗 AI Landing Zone (GitHub): https://github.com/Azure/AI-Landing-Zones?tab=readme-ov-file 👉 Start here if you want a standardized baseline before onboarding any AI workloads. 2️⃣ AI Hub Gateway – Solution Accelerator Purpose: Centralize and control access to AI services across multiple teams or customers. The AI Hub Gateway Solution Accelerator helps you: Expose AI capabilities (models, agents, APIs) via a centralized gateway Apply consistent security, routing, and traffic controls Support both Chat UI and API-based consumption Enable multi-team or multi-tenant AI usage patterns 🔗 AI Hub Gateway Solution Accelerator: https://github.com/mohamedsaif/ai-hub-gateway-landing-zone?tab=readme-ov-file 👉 Ideal when you want a shared AI platform with controlled access and visibility. 3️⃣ Citadel Governance Hub (Advanced Governance) Purpose: Enforce strong governance, compliance, and guardrails for AI usage. The Citadel Governance Hub builds on top of the AI Hub Gateway and focuses on: Policy enforcement for AI usage Centralized governance controls Secure onboarding of teams and workloads Alignment with enterprise risk and compliance requirements 🔗 Citadel Governance Hub (README): https://github.com/Azure-Samples/ai-hub-gateway-solution-accelerator/blob/citadel-v1/README.md 👉 Recommended for regulated environments or large enterprises with strict governance needs. 4️⃣ AKS Cost Analysis (Operational Excellence) Purpose: Understand and optimize the cost of running AI workloads on AKS. AI platforms often rely on AKS for agents, inference services, and gateways. This guide explains: How AKS costs are calculated How to analyze node, pod, and workload costs Techniques to optimize cluster spend 🔗 AKS Cost Analysis: https://learn.microsoft.com/en-us/azure/aks/cost-analysis 👉 Use this early to avoid unexpected cost overruns as AI usage scales. 5️⃣ AKS Multi-Tenancy & Cluster Isolation Purpose: Safely run workloads for multiple teams or customers on AKS. This guidance covers: Namespace vs cluster isolation strategies Security and blast-radius considerations When to use shared clusters vs dedicated clusters Best practices for multi-tenant AKS platforms 🔗 AKS Multi-Tenancy & Cluster Isolation: https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-cluster-isolation 👉 Critical reading if your AI platform supports multiple teams, business units, or customers. 🧭 Suggested Learning Path If you’re new, follow this order: AI Landing Zone → build the foundation AI Hub Gateway → centralize AI access Citadel Governance Hub → enforce guardrails AKS Cost Analysis → control spend AKS Multi-Tenancy → scale securelyFrom Large Semi-Structured Docs to Actionable Data: In-Depth Evaluation Approaches Guidance
Introduction Extracting structured data from large, semi-structured documents (the detailed solution implementation overview and architecture is provided in this tech community blog: From Large Semi-Structured Docs to Actionable Data: Reusable Pipelines with ADI, AI Search & OpenAI) demands a rigorous evaluation framework. The goal is to ensure our pipeline is accurate, reliable, and scalable before we trust it with mission-critical data. This framework breaks evaluation into clear phases, from how we prepare the document, to how we find relevant parts, to how we validate the final output. It provides metrics, examples, and best practices at each step, forming a generic pattern that can be applied to various domains. Framework Overview A very structured and stepwise approach for evaluation is given below: Establish Ground Truth & Sampling: Define a robust ground truth set and sampling method to fairly evaluate all parts of the document. Preprocessing Evaluation: Verify that OCR, chunking, and any structural augmentation (like adding headers) preserve all content and context. Labelling Evaluation: Check classification of sections/chunks by content based on topic/entity and ensure irrelevant data is filtered out without losing any important context. Retrieval Evaluation: Ensure the system can retrieve the right pieces of information (using search) with high precision@k and recall@k. Extraction Accuracy Evaluation: Measure how well the final structured data matches the expected values (field accuracy, record accuracy, overall precision/recall). Continuous Improvement Loop with SME: Use findings to retrain, tweak, and improve, enabling the framework to be reused for new documents and iterations. SMEs play a huge role in such scenarios. Detailed Guidance on Evaluation Below is a step-by-step, in-depth guide to evaluating this kind of IDP (Indelligent Document Processing) pipeline, covering both the overall system and its individual components: Establish Ground Truth & Sampling Why: Any evaluation is only as good as the ground truth it’s compared against. Start by assembling a reliable “source of truth” dataset for your documents. This often means manual labelling of some documents by domain experts (e.g., a legal team annotating key clauses in a contract, or accountants verifying invoice fields). Because manual curation is expensive, be strategic in what and how we sample. Ground Truth Preparation: Identify the critical fields and sections we need to extract, and create an annotated set of documents with those values marked correct. For example, if processing financial statements, we might mark the ground truth values for Total Assets, Net Income, Key Ratios, etc. This ground truth should be the baseline to measure accuracy against. Although creating it is labour-intensive, it yields a precise benchmark for model performance. Stratified Sampling: Documents like contracts or policies have diverse sections. To evaluate thoroughly, use stratified sampling – ensure your test set covers all major content types and difficulty levels. For instance, if 15% of pages in a set of contracts are annexes or addendums, then ~15% of your evaluation pages should come from annexes, not just the main body. This prevents the evaluation from overlooking challenging or rare sections. In practice, we might partition a document by section type (clauses, tables, schedules, footnotes) and sample a proportion from each. This way, metrics reflect performance on each type of content, not just the easiest portions. Multi-Voter Agreement (Consensus): It’s often helpful to have multiple automated voters on the outputs before involving humans. For example, suppose we extracted an invoice amount; we can have: A regex/format checker/fuzzy matching voter A cross-field logic checker/embedding based matching voter An ML model confidence score/LLM as a judge vote If all signals are strong, we label that extraction as Low Risk; if they conflict, mark it High Risk for human review. By tallying such “votes”, we create tiers of confidence. Why? Because in many cases, a large portion of outputs will be obviously correct (e.g., over 80% might have unanimous high confidence), and we can safely assume those are right, focusing manual review on the remainder. This strategy effectively reduces the human workload while maintaining quality. Preprocessing Evaluation Before extracting meaning, make sure the raw text and structure are captured correctly. Any loss here breaks the whole pipeline. Key evaluation checks: OCR / Text Extraction Accuracy Character/Error Rate: Sample pages to see how many words are recognized correctly (use per-word confidence to spot issues). Layout Preservation: Ensure reading order isn’t scrambled, especially in multi-column pages or footnotes. Content Coverage: Verify every sentence from a sample page appears in the extracted text. Missing footers or sidebars count as gaps. Chunking Completeness: Combined chunks should reconstruct the full document. Word counts should match. Segment Integrity: Chunks should align to natural boundaries (paragraphs, tables). Track a metric like “95% clean boundaries.” Context Preservation: If a table or section spans chunks, mark relationships so downstream logic sees them as connected. Multi-page Table Handling Header Insertion Accuracy: Validate that continued pages get the correct header (aim for high 90% to maintain context across documents). No False Headers: Ensure new tables aren’t mistakenly treated as continuations. Track a False Continuation Rate and push it to near zero. Practical Check: Sample multi-page tables across docs to confirm consistent extraction and no missed rows. Structural Links / References Link Accuracy: Confirm references (like footnotes or section anchors) map to the right targets (e.g., 98%+ correct). Ontology / Category Coverage: If content is pre-grouped, check precision (no mis-grouping) and recall (nothing left uncategorized). Implication The goal is to ensure the pre-processed chunks are a faithful, complete, and structurally coherent representation of the original document. Metrics like content coverage, boundary cleanliness, and header accuracy help catch issues early. Fixing them here saves significant downstream debugging. Labelling Evaluation – “Did we isolate the right pieces?” Once we chunk the document, we label those chunks (with ML or rules) to map them to the right entities and throw out the noise. Think of this step as sorting useful clauses from filler. Section/Entity Labelling Accuracy Treat labelling as a multi-class or multi-label classification problem. Precision (Label Accuracy): Of the chunks we labelled as X, how many were actually X? Example: Model tags 40 chunks as “Financial Data.” If 5 are wrong, precision is 87.5. High precision avoids polluting a category (topic/entity) with junk. Recall (Coverage): Of the chunks that truly belong to category X, how many did we catch? Example: Ground truth has 50 Financial Data chunks, model finds 45. Recall is 90%. High recall prevents missing important sections. Example: A model labels paper sections as Introduction, Methods, Results, etc. It marks 100 sections as Results and 95 are correct (95% precision). It misses 5 actual Results (slightly lower recall). That’s acceptable if downstream steps can still recover some items. But low precision means the labelling logic needs tightening. Implication Low precision means wrong info contaminates the category. Low recall means missing crucial bits. Use these metrics to refine definitions or adjust the labelling logic. Don’t just report one accuracy number; precision and recall per label tell the real story. Retrieval Evaluation – “Can we find the right info when we ask?” Many document pipelines use retrieval to narrow a huge file down to the few chunks most likely to contain the answer corresponding to a topic/entity. If we need a “termination date,” we first fetch chunks about dates or termination, then extract from those. Retrieval must be sharp, or everything downstream suffers. Precision@K How many of the top K retrieved chunks are actually relevant? If we grab 5 chunks for “Key Clauses” and 4 are correct, Precision@5 is 80%. We usually set K to whatever the next stage consumes (3 or 5). High precision keeps extraction clean. Average it across queries or fields. Critical fields may demand very high Precision@K. Recall@K Did we retrieve enough of the relevant chunks? If there are 2 relevant chunks in the doc but the top 5 results include only 1, recall is 50%. Good recall means we aren’t missing mentions in other sections or appendices. Increasing K improves recall but can dilute precision. Tune both together. Ranking Quality (MRR, NDCG) If order matters, use rank-aware metrics. MRR: Measures how early the first relevant result appears. Perfect if it’s always at rank 1. NDCG@K: Rewards having the most relevant chunks at the top. Useful when relevance isn’t binary. Most pipelines can get away with Precision@K and maybe MRR. Implication Test 50 QA pairs from policy documents, retrieving 3 passages per query. Average Precision@3: 85%. Average Recall@3: 92%. MRR: 0.8. Suppose, we notice “data retention” answers appear in appendices that sometimes rank low. We increase K to 5 for that query type. Precision@3 rises to 90%, and Recall@5 hits roughly 99%. Retrieval evaluation is a sanity check. If retrieval fails, extraction recall will tank no matter how good the extractor is. Measure both so we know where the leak is. Also keep an eye on latency and cost if fancy re-rankers slow things down. Extraction Accuracy Evaluation – “Did we get the right answers?” Look at each field and measure how often we got the right value. Precision: Of the values we extracted, what percent are correct? Use exact match or a lenient version if small format shifts don’t matter. Report both when useful. Recall: Out of all ground truth values, how many did we actually extract? Per-field breakdown: Some fields will be easy (invoice numbers, dates), others messy (vendor names, free text). A simple table makes this obvious and shows where to focus improvements. Error Analysis Numbers don’t tell the whole story. Look at patterns: OCR mix-ups Bad date or amount formats Wrong chunk retrieved upstream Misread tables Find the recurring mistakes. That’s where the fixes live. Holistic Metrics If needed, compute overall precision/recall across all extracted fields. But per-field and record-level are usually what matter to stakeholders. Implication Precision protects against wrong entries. Recall protects against missing data. Choose your balance based on risk: If false positives hurt more (wrong financial numbers), favour precision. If missing items hurts more (missing red-flag clauses), favour recall. Continuous Improvement Loop with SME Continuous improvement means treating evaluation as an ongoing feedback loop rather than a one-time check. Each phase’s errors point to concrete fixes, and every fix is re-measured to ensure accuracy moves in the right direction without breaking other components. The same framework also supports A/B testing alternative methods and monitoring real production data to detect drift or new document patterns. Because the evaluation stages are modular, they generalize well across domains such as contracts, financial documents, healthcare forms, or academic papers with only domain-specific tweaks. Over time, this creates a stable, scalable and measurable path toward higher accuracy, better robustness, and easier adaptation to new document types. Conclusion Building an end-to-end evaluation framework isn’t just about measuring accuracy, it’s about creating trust in the entire pipeline. By breaking the process into clear phases, defining robust ground truth, and applying precision/recall-driven metrics at every stage, we ensure that document processing systems are reliable, scalable, and adaptable. This structured approach not only highlights where improvements are needed but also enables continuous refinement through SME feedback and iterative testing. Ultimately, such a framework transforms evaluation from a one-time exercise into a sustainable practice, paving the way for higher-quality outputs across diverse domains.
