Warning
Important Update We've made major updates to Agentic Content Processing that include breaking changes. If you need the previous version, you can still find it in the v1 branch.
Process multi-document claims by extracting data from each document, applying schemas with confidence scoring, and generating AI-powered summaries and gap analysis across the entire claim. Upload multiple files — invoices, forms, images, contracts — to a single claim, and the solution automatically processes each document through a multi-modal content extraction pipeline, then orchestrates cross-document summarization and gap identification using an Agent Framework Workflow Engine.
The core content processing engine supports text, images, tables and graphs with schema-based transformation and confidence scoring. These capabilities can be applied to numerous use cases including: insurance claims processing, contract review, invoice processing, ID verification, and logistics shipment record processing.
Note: With any AI solutions you create using these templates, you are responsible for assessing all associated risks and for complying with all applicable laws and safety standards. Learn more in the transparency documents for Agent Service and Agent Framework.
This accelerator leverages Azure AI Foundry, Azure AI Content Understanding Service, Azure OpenAI Service GPT-5.1, Azure Blob Storage, Azure Cosmos DB, and Azure Container Apps to process multi-document claims through a two-level architecture:
- Claim Processing Workflow — Upload multiple documents to a claim via the Web UI. The Content Process Workflow (built on the Agent Framework Workflow Engine) orchestrates document extraction, AI-powered summarization, and gap analysis across all documents in the claim.
- Content Processing Pipeline — The core engine (carried forward from v1) that processes each individual document through a 4-stage pipeline: Extract → Map → Evaluate → Save, with confidence scoring for extraction accuracy and schema mapping.
Processing, extraction, schema transformation, summarization, and gap analysis steps are tracked with status and scored for accuracy to automate processing and identify as-needed human validation.
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Click to view detailed architecture diagram
graph TB
subgraph UserInterface["🖥️ User Interface"]
WEB["<b>Claim Process Monitor Web</b><br/>(React / TypeScript / NGINX)<br/>ca-name-web"]
end
subgraph API_Layer["🔗 Content Process API — Gateway"]
API["<b>Content Process API</b><br/>(FastAPI / Python)<br/>ca-name-api"]
API_WF["/claimprocessor/*<br/>Workflow Endpoints"]
API_CP["/contentprocessor/*<br/>Content Processor Endpoints"]
API_SV["/schemasetvault/*<br/>Schema Set Endpoints"]
API_SC["/schemavault/*<br/>Schema Endpoints"]
API --- API_WF
API --- API_CP
API --- API_SV
API --- API_SC
end
subgraph Queues["📨 Azure Storage Queues"]
Q_CLAIM["claim-process-queue"]
Q_DLQ["claim-process-dead-letter-queue"]
Q_EXTRACT["content-pipeline-extract-queue"]
end
subgraph Workflow["⚙️ Content Process Workflow — Agent Framework"]
WF["<b>Content Process Workflow</b><br/>(Agent Framework Workflow Engine)<br/>ca-name-wkfl"]
WF_S1["Stage 1: Document Processing<br/>(DocumentProcessExecutor)<br/>Invokes Content Processor per document"]
WF_S2["Stage 2: Summarizing<br/>(SummarizeExecutor)<br/>AI summary across all docs"]
WF_S3["Stage 3: Gap Analysis<br/>(GapExecutor)<br/>AI gap identification"]
WF --> WF_S1 --> WF_S2 --> WF_S3
end
subgraph Processor["📄 Content Processor — 4-Stage Pipeline"]
CP["<b>Content Processor</b><br/>(Python / Queue Worker)<br/>ca-name-app"]
CP_E["1. Extract<br/>(Azure AI Content Understanding)"]
CP_M["2. Map<br/>(GPT-5.1 Vision)"]
CP_V["3. Evaluate<br/>(Merge & Score)"]
CP_S["4. Save<br/>(Blob + Cosmos DB)"]
CP --> CP_E --> CP_M --> CP_V --> CP_S
end
subgraph AzureAI["🧠 Azure AI Services"]
AICU["<b>Azure AI Content<br/>Understanding</b>"]
AOAI["<b>Azure OpenAI</b><br/>GPT-5.1"]
end
subgraph DataStores["💾 Data & Storage"]
BLOB["<b>Azure Blob Storage</b><br/>Documents, Manifests, Results"]
COSMOS["<b>Azure Cosmos DB</b><br/>Processes | Schemas | claimprocesses"]
end
subgraph Config["🔧 Configuration & Infrastructure"]
APPCONFIG["<b>App Configuration</b>"]
ACR["<b>Container Registry</b>"]
CAE["<b>Container App Environment</b>"]
LOG["<b>Log Analytics</b>"]
end
%% Main flow
WEB -->|"HTTP"| API
API_WF -->|"enqueue claim"| Q_CLAIM
Q_CLAIM -->|"dequeue"| WF
WF_S1 -->|"HTTP per document"| API_CP
API_CP -->|"enqueue document"| Q_EXTRACT
Q_EXTRACT -->|"dequeue"| CP
WF -->|"failed messages"| Q_DLQ
%% AI service connections
CP_E -->|"OCR & layout"| AICU
CP_M -->|"vision extraction"| AOAI
WF_S2 -->|"summarization"| AOAI
WF_S3 -->|"gap analysis"| AOAI
%% Data store connections
CP_S -->|"save results"| BLOB
CP_S -->|"save results"| COSMOS
WF -->|"claim status & results"| COSMOS
WF_S1 -->|"download manifest"| BLOB
API -->|"read/write"| COSMOS
API -->|"read/write"| BLOB
%% Config connections
API -.->|"settings"| APPCONFIG
WF -.->|"settings"| APPCONFIG
CP -.->|"settings"| APPCONFIG
ACR -.->|"images"| CAE
CAE -.-> LOG
The claim processing workflow is built on the Agent Framework's Workflow Engine — a DAG-based event-streaming execution model that orchestrates specialized AI agents across the claim lifecycle. Each stage is an autonomous Executor that receives context, performs its task, and passes results downstream.
Click to view detailed agentic architecture diagram
flowchart TB
subgraph Trigger["📨 Trigger"]
QUEUE["Azure Storage Queue<br/>claim-process-queue"]
end
subgraph WorkflowEngine["⚙️ Agent Framework — Workflow Engine"]
direction TB
WB["WorkflowBuilder<br/><i>DAG graph construction</i>"]
subgraph Agent1["🤖 Agent 1 — DocumentProcessExecutor"]
A1_DESC["For each document in the claim:<br/>• Submit to Content Processor API<br/>• Trigger 4-stage extraction pipeline<br/>(Extract → Map → Evaluate → Save)<br/>• Poll for completion & collect results"]
end
subgraph Agent2["🤖 Agent 2 — SummarizeExecutor"]
A2_DESC["Consolidated AI summary:<br/>• Receives all extraction results<br/>• Sends to Azure OpenAI GPT-5.1<br/>• Generates cross-document summary<br/>(claimant, vehicle, incident, costs)"]
end
subgraph Agent3["🤖 Agent 3 — GapExecutor"]
A3_DESC["Gap & discrepancy analysis:<br/>• Evaluates YAML DSL ruleset<br/>• Detects missing documents<br/>• Flags cross-document discrepancies<br/>• Sends to Azure OpenAI GPT-5.1"]
end
WB --> Agent1 --> Agent2 --> Agent3
end
subgraph AIServices["🧠 Azure AI Services"]
AICU["Azure AI Content<br/>Understanding"]
AOAI["Azure OpenAI<br/>GPT-5.1"]
end
subgraph Persistence["💾 Results"]
COSMOS["Azure Cosmos DB"]
BLOB["Azure Blob Storage"]
end
QUEUE --> WorkflowEngine
Agent1 -->|"OCR & layout"| AICU
Agent1 -->|"vision extraction"| AOAI
Agent2 -->|"summarization"| AOAI
Agent3 -->|"gap analysis"| AOAI
Agent3 -->|"persist results"| COSMOS
Agent3 -->|"persist results"| BLOB
| Capability | Detail |
|---|---|
| Execution model | DAG-based workflow with event streaming, concurrent workers, and retry logic |
| Agent orchestration | WorkflowBuilder registers executors, defines edges, and builds a frozen Workflow graph |
| Executor pattern | Each agent is an Executor subclass with @handler-decorated async methods |
| Fault tolerance | Exponential backoff retries, dead-letter queue (claim-process-dead-letter-queue), graceful shutdown |
| Extensibility | Add new agents (executors) and edges to the DAG without modifying existing stages |
For full details, see Claim Processing Workflow (Agent Framework).
If you'd like to customize the solution accelerator, here are some common areas to start:
Adding your own Schemas and Data
Modifying System Processing Prompts
Gap Analysis Ruleset Guide (YAML DSL — no-code rule authoring)
API Reference for Content Processing & Claim Management
Customizing the Claim Processing Workflow
Document Processing Pipeline (4-stage extraction)
Claim Processing Workflow (Agent Framework)
Golden Path Workflows (end-to-end walkthroughs)
Click to learn more about the key features this solution enables
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Multi-document claim processing
Upload multiple files to a single claim and process them as a batch. The claim workflow orchestrates content extraction for each document, then performs cross-document summarization and gap analysis. -
Multi-modal content processing
Core extraction engine utilizes machine learning-based OCR for efficient text extraction and integrates GPT-5.1 Vision for processing various content formats including text, images, tables, and graphs. -
AI-powered summarization & gap analysis
After all documents in a claim are processed, GPT-5.1 generates a consolidated summary and performs gap analysis — detecting missing documents and flagging cross-document discrepancies across the claim. -
No-code gap analysis ruleset (YAML DSL)
Gap analysis rules are defined in a reusable YAML-based Domain-Specific Language — domain experts can add, modify, or replace rules without writing code. The same DSL format is portable across industries (insurance, logistics, legal, finance). See Gap Analysis Ruleset Guide. -
Agent Framework Workflow Engine
Claim processing is orchestrated by a DAG-based workflow engine with event streaming, concurrent workers, retry logic, and dead-letter queue support for production reliability. -
Schema-based data transformation
Maps extracted content to custom or industry-defined schemas and outputs as JSON for interoperability. -
Confidence scoring
Calculation of entity extraction and schema mapping processes for accuracy, providing scores to drive manual human-in-the-loop review, if desired. -
Review, validate, update
Transparency in reviewing processing steps, summaries, and gap analysis — allowing for review, comparison to source asset, ability to modify output results, and annotation for historical reference. -
API driven processing pipelines
API endpoints are available for claim lifecycle management, content processing, schema management, and external source system integration.
Follow the quick deploy steps on the deployment guide to deploy this solution to your own Azure subscription.
Note: This solution accelerator requires Azure Developer CLI (azd) version 1.18.0 or higher. Please ensure you have the latest version installed before proceeding with deployment. Download azd here.
Click here to launch the deployment guide
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⚠️ Important: Check Azure OpenAI Quota Availability
To ensure sufficient quota is available in your subscription, please follow quota check instructions guide before you deploy the solution.
🛠️ Need Help? Check our Troubleshooting Guide for solutions to common deployment issues.
To deploy this solution accelerator, ensure you have access to an Azure subscription with the necessary permissions to create resource groups, resources, app registrations, and assign roles at the resource group level. This should include Contributor role at the subscription level and Role Based Access Control role on the subscription and/or resource group level. Follow the steps in Azure Account Set Up.
Here are the supported regions for deployment: Australia East, Central US, East Asia, East US 2, Japan East, North Europe, Southeast Asia, UK South.
Check the Azure Products by Region page and select a region where the following services are available.
Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers. However, Azure Container Registry has a fixed cost per registry per day.
Use the Azure pricing calculator to calculate the cost of this solution in your subscription. Review a sample pricing sheet for the architecture.
| Product | Description | Cost |
|---|---|---|
| Azure AI Foundry | Build generative AI applications on an enterprise-grade platform | Pricing |
| Azure OpenAI Service | Provides REST API access to OpenAI's powerful language models including GPT-5.1 for content extraction, summarization, and gap analysis | Pricing |
| Azure AI Content Understanding Service | Analyzes various media content—such as audio, video, text, and images—transforming it into structured, searchable data | Pricing |
| Azure Blob Storage | Microsoft's object storage solution for the cloud. Blob storage is optimized for storing massive amounts of unstructured data | Pricing |
| Azure Container Apps | Allows you to run containerized applications without worrying about orchestration or infrastructure. | Pricing |
| Azure Container Registry | Build, store, and manage container images and artifacts in a private registry for all types of container deployments | Pricing |
| Azure Cosmos DB | Fully managed, distributed NoSQL, relational, and vector database for modern app development | Pricing |
| Azure Queue Storage | Store large numbers of messages and access messages from anywhere in the world via HTTP or HTTPS. | Pricing |
| GPT Model Capacity | GPT-5.1 model with multimodal capabilities, accepting both text and images as input for extraction, summarization, and gap analysis | Pricing |
⚠️ Important: To avoid unnecessary costs, remember to take down your app if it's no longer in use, either by deleting the resource group in the Portal or runningazd down.
For detailed cost estimation and pricing information, see the Deployment Guide.
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The included sample scenario demonstrates a First Notice of Loss (FNOL) workflow for an auto insurance company. A claims analyst receives incoming collision claims — each containing an auto insurance claim form, police report, repair estimate, and photos of vehicle damage — that need to be processed together.
The analyst creates a claim in the system and uploads all documents. The workflow engine automatically:
- Extracts structured data from each document — policyholder info, vehicle details (VIN, plate), incident description, and repair line items — using AI Content Understanding and GPT-5.1 Vision.
- Summarizes findings across the entire claim — producing a consolidated report covering claimant details, vehicle info, incident narrative, damage assessment, and estimated costs.
- Runs gap & discrepancy analysis using YAML DSL rules — detecting missing documents (e.g., no police report for a theft claim, no repair estimate when the loss exceeds $2,000) and flagging cross-document discrepancies (e.g., mismatched claim numbers, VIN conflicts between the claim form and police report, or date-of-loss differences).
The analyst reviews the AI-generated summary for a quick overview, checks gap analysis results for flagged issues and discrepancies, and drills into individual document extractions when needed. What previously required hours of manual cross-referencing is now orchestrated in minutes.
Sample data included: The
claim_date_of_loss/folder provides a complete claim package (claim form + police report + repair estimate + damage photo). Theclaim_hail/folder provides a claim with a subset of documents (claim form + repair estimate + damage photo) to demonstrate gap detection. See Golden Path Workflows for a step-by-step walkthrough.
Click to learn more about what value this solution provides
-
Automated claim intake
Upload claim forms, police reports, repair estimates, and damage photos to a single claim. The workflow engine orchestrates extraction, summarization, and gap analysis automatically — reducing intake processing from hours to minutes. -
Cross-document intelligence
AI-powered summarization consolidates findings across all documents in a claim, while gap analysis identifies missing documents and flags discrepancies — such as mismatched claim numbers, VIN conflicts, or date-of-loss differences between the claim form and police report. -
No-code gap rules
Gap analysis rules are defined in a YAML DSL that domain experts can modify without writing code — add required document checks, adjust severity levels, or define new discrepancy rules across industries. -
Confidence-driven human-in-the-loop
Extraction and schema mapping are scored for accuracy using dual confidence signals (OCR-level and model log-probability), so analysts can focus human review on low-confidence results while high-confidence data flows through automatically. -
Verifiable review & audit trail
Review AI-generated extractions, summaries, and gap analysis results side-by-side with source documents. Annotate changes, add comments, and compare processing steps for transparency and audit readiness. -
Responsible AI safety gate
A built-in RAI executor screens every document's extracted content against 10 safety categories — including self-harm, violence, prompt injection, and discriminatory content — before further processing, helping ensure only safe content reaches downstream workflows. -
Schema-driven extensibility
Define custom Pydantic schemas to extract structured data from any document type. The same extraction pipeline generalizes across industries — insurance, logistics, legal, finance — without code changes. -
Production-ready orchestration
The Agent Framework Workflow Engine provides DAG-based execution with event streaming, retry logic, dead-letter queues, and graceful shutdown — designed for reliable, scalable processing in production environments. -
API-first integration
All capabilities — claim lifecycle, content processing, schema management — are exposed through REST APIs, enabling integration with existing line-of-business systems, RPA workflows, and custom applications.
This template uses Azure App Configuration for centralized configuration management and Managed Identity for secure service-to-service authentication — no connection strings or secrets are stored in application code.
To ensure continued best practices in your own repository, we recommend that anyone creating solutions based on our templates ensure that the Github secret scanning setting is enabled.
You may want to consider additional security measures, such as:
- Enabling Microsoft Defender for Cloud to secure your Azure resources.
- Protecting the Azure Container Apps instance with a firewall and/or Virtual Network.
Check out similar solution accelerators
| Solution Accelerator | Description |
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| Document knowledge mining | Process and extract summaries, entities, and metadata from unstructured, multi-modal documents and enable searching and chatting over this data. |
| Conversation knowledge mining | Derive insights from volumes of conversational data using generative AI. It offers key phrase extraction, topic modeling, and interactive chat experiences through an intuitive web interface. |
| Document generation | Identify relevant documents, summarize unstructured information, and generate document templates. |
Have questions, find a bug, or want to request a feature? Submit a new issue on this repo and we'll connect.
Please refer to Transparency FAQ for responsible AI transparency details of this solution accelerator.
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