Accelerators are a Microsoft term for complete, working applications that serve as production-ready starting points for your own solutions. Rather than building from scratch, accelerators let you:
- ✅ Learn faster - See real-world implementations of AI patterns and best practices
- ✅ Build securely - Start with security, compliance, and responsible AI already built in
- ✅ Ship sooner - Extend working code instead of writing boilerplate from zero
- ✅ Reduce risk - Leverage tested, validated architectures designed for government use
Think of accelerators as fully-functional blueprints - they work out of the box, but are designed for you to customize and extend for your specific agency needs.
This repository contains 7 AI accelerators designed to transform how NY State government agencies serve constituents. Each accelerator is a complete application built with Microsoft Azure AI services and the Semantic Kernel framework, demonstrating practical AI solutions for:
| Challenge | Accelerator Solution |
|---|---|
| 📞 Citizens can't find answers | AI chatbot with citations |
| 📄 Document processing backlogs | Automated OCR & validation |
| 🚨 Emergency coordination gaps | Multi-agent planning system |
| 📋 Policy compliance burden | Automated document review |
| 🔍 Siloed agency knowledge | Cross-agency secure search |
| 🏙️ NYC citizen services (.NET) | RAG-powered .NET chatbot |
| 🤖 NYC citizen services (Python) | RAG-powered Python chatbot |
All accelerators comply with NY State's LOADinG Act and RAISE Act requirements for transparent, accountable AI in government.
| Version | Date | Changes | Status |
|---|---|---|---|
| 2.2.0 | Jan 13, 2026 | Added Python Virtual Citizen Assistant, 267 tests | ✅ Current |
| 2.1.0 | Jan 12, 2026 | Added .NET Virtual Citizen Assistant, 265 tests | ✅ Stable |
| 2.0.0 | Jan 12, 2026 | Production release with 5 accelerators | ✅ Stable |
| 1.5.0 | Jan 10, 2026 | Added Inter-Agency Knowledge Hub accelerator | ✅ Stable |
| 1.4.0 | Jan 9, 2026 | Added Policy Compliance Checker accelerator | ✅ Stable |
| 1.3.0 | Jan 8, 2026 | Added Emergency Response Agent accelerator | ✅ Stable |
| 1.2.0 | Jan 7, 2026 | Added Document Eligibility Agent accelerator | ✅ Stable |
| 1.1.0 | Jan 6, 2026 | Added Constituent Services Agent accelerator | ✅ Stable |
| 1.0.0 | Jan 5, 2026 | Initial repository setup with shared infrastructure | ✅ Stable |
🎯 Purpose: AI-powered chatbot answering citizen questions about NY State services
✨ Key Features:
- 💬 Natural language Q&A about SNAP benefits, driver's licenses, unemployment, Medicaid
- 📚 Citation-backed responses with source documents
- 📊 Confidence scoring and human escalation when uncertain
- 🌍 Multi-language support (English, Spanish, Chinese, Arabic, Russian, Korean, Haitian Creole, Bengali)
- ♿ WCAG 2.1 AA accessible web interface
🛠️ Tech Stack: Azure AI Foundry + Foundry IQ + Semantic Kernel + Flask
cd Constituent-Services-Agent
pip install -r requirements.txt
python demo.py💡 Sample Queries:
- "How do I apply for SNAP benefits?"
- "How do I renew my driver's license?"
- "Am I eligible for Medicaid?"
🎯 Purpose: Automated processing of eligibility documents (W-2s, pay stubs, utility bills)
✨ Key Features:
- 📧 Email inbox monitoring for document submissions
- 🔍 OCR and intelligent data extraction using Azure Document Intelligence
- 📊 Confidence scoring for all extracted fields
- 🔒 PII detection and automatic masking
- ✅ Validation rules (document age, completeness)
- 📋 Case routing and workload distribution
🛠️ Tech Stack: Azure Document Intelligence + Microsoft Graph + Semantic Kernel + Flask
cd Document-Eligibility-Agent
pip install -r requirements.txt
python demo.py📄 Supported Document Types:
| Document | Fields Extracted |
|---|---|
| W-2 Forms | Wages, employer, tax year |
| Pay Stubs | Gross pay, period, date |
| Utility Bills | Provider, address, date |
| Bank Statements | Institution, balance |
| Driver's Licenses | Name, DOB, expiration |
| Birth Certificates | Name, DOB, parents |
| Lease Agreements | Landlord, address, rent |
🎯 Purpose: Multi-agent system for emergency response planning and coordination
✨ Key Features:
- 🌀 Emergency scenario simulation (hurricane, fire, flood, winter storm, public health, earthquake)
- 🌤️ Real-time weather integration
- 🏛️ Multi-agency resource coordination (FDNY, NYPD, OEM, DOT, MTA)
- 🚗 Evacuation route planning with bottleneck analysis
- 📜 Historical incident analysis for lessons learned
- ⏱️ Response plans with timeline milestones
🛠️ Tech Stack: Semantic Kernel + Azure AI Foundry + Weather APIs + Multi-Agent Orchestration
🚨 Supported Emergency Types:
| Type | Lead Agency | Key Resources |
|---|---|---|
| 🌀 Hurricane | OEM | Evacuation, shelters |
| 🔥 Fire | FDNY | Firefighters, equipment |
| 🌊 Flooding | OEM | Pumps, rescue boats |
| ❄️ Winter Storm | DOT | Plows, salt trucks |
| 🏥 Public Health | DOH | Healthcare workers, vaccines |
| 🏚️ Earthquake | OEM | Search & rescue teams |
| ⚡ Infrastructure | Utilities | Emergency generators |
🎯 Purpose: Automated review of policy documents against compliance rules
✨ Key Features:
- 📄 Document parsing (PDF, DOCX, Markdown)
- 🔍 Rule-based compliance checking with regex patterns
⚠️ Severity categorization (Critical, High, Medium, Low)- 📊 Compliance scoring (0-100)
- 🤖 AI-powered analysis with Azure OpenAI
- 💡 Detailed recommendations for each violation
- 🔄 Version comparison for policy changes
🛠️ Tech Stack: Azure AI Foundry + Semantic Kernel + Document AI
cd Policy-Compliance-Checker
pip install -r requirements.txt
python demo.py📋 Compliance Categories:
| Category | Description | Examples |
|---|---|---|
| Data Privacy | PII handling rules | Encryption, retention |
| Accessibility | WCAG compliance | Alt text, contrast |
| Security | Security standards | Authentication, logging |
| Documentation | Policy requirements | Version control, approval |
🎯 Purpose: Cross-agency document search with permission-aware results
✨ Key Features:
- 🔍 Unified search across 5+ agency knowledge bases (DMV, DOL, OTDA, DOH, OGS)
- 🔐 Entra ID authentication with role-based access
- 🛡️ Permission-aware result filtering
- 📚 Citation tracking for LOADinG Act compliance
- 🔗 Cross-agency policy cross-references
- 👤 Human-in-the-loop for complex queries
- 📋 7-year audit log retention
🛠️ Tech Stack: Microsoft Foundry + Foundry IQ + Azure AI Search + Entra ID
cd Inter-Agency-Knowledge-Hub
pip install -r requirements.txt
python demo.py🏛️ Supported Agencies:
| Agency | Domain | Documents |
|---|---|---|
| DMV | Transportation | Licensing, registration |
| DOL | Labor | Employment, wages |
| OTDA | Social Services | Benefits, assistance |
| DOH | Health | Public health, regulations |
| OGS | General Services | Procurement, facilities |
🎯 Purpose: RAG-powered AI assistant for NYC government services built with .NET
✨ Key Features:
- 💬 AI chat assistant with source citations
- 🔍 Semantic, keyword, and hybrid search modes
- 📂 Category browser with visual grid layout
- 📄 Document details with print and share
- 🛠️ Data upload utility for Azure AI Search
- 🎨 Bootstrap 5.3 responsive UI
🛠️ Tech Stack: .NET 9 + ASP.NET Core MVC + Semantic Kernel 1.65 + Azure AI Search + Azure OpenAI
cd DotNet-Virtual-Citizen-Assistant
dotnet restore
dotnet run --project VirtualCitizenAgent💡 Sample Features:
- Chat with AI about NYC services
- Search documents semantically
- Browse by service category
🎯 Purpose: RAG-powered AI assistant for city government services built with Python
✨ Key Features:
- 💬 Natural language Q&A about city services (trash pickup, permits, emergency alerts)
- 🔍 Vector search + keyword search + hybrid search modes
- 🔌 Plugin architecture with Semantic Kernel 1.37
- 📅 Appointment scheduling with mock service
- 📚 Citation-backed responses with source documents
- 🧪 Built-in test framework for validation
🛠️ Tech Stack: Semantic Kernel 1.37 + Azure AI Search + Azure OpenAI + Flask
cd Virtual-Citizen-Assistant
pip install -r requirements.txt
python test_setup.py # Validate setup
python test_plugins.py # Test plugins
python src/main.py # Run interactive assistant💡 Sample Queries:
- "When is my next trash pickup?"
- "How do I apply for a business permit?"
- "Are there any current emergency alerts in my area?"
🔌 Available Plugins:
| Plugin | Functions | Purpose |
|---|---|---|
| DocumentRetrieval | search_city_services, get_service_by_category | Search city service information |
| Scheduling | check_availability, scheduling_info, list_schedulable_services | Appointment management |
┌─────────────────────────────────────────────────────────────────┐
│ 🖥️ Frontend Layer │
│ Flask Web UI │ REST APIs │ WCAG 2.1 AA Accessible │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ 🤖 AI Orchestration Layer │
│ Semantic Kernel │ Foundry IQ │ Multi-Agent Patterns │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ ☁️ Azure AI Services │
│ Azure OpenAI GPT-4o │ Document Intelligence │ AI Search │
│ Microsoft Graph │ Translator │ Entra ID │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ 💾 Data Layer │
│ SQLite/Azure SQL │ Blob Storage │ Vector DBs │
└─────────────────────────────────────────────────────────────────┘
- ✅ All AI decisions are logged with rationale
- ✅ Human-in-the-loop for benefits determinations
- ✅ Transparent citation of data sources
- ✅ Bias testing across demographic groups
- ✅ AI assistance clearly disclosed to users
- ✅ Accountability measures for automated decisions
- ✅ Regular auditing and evaluation frameworks
- ✅ Azure AI Evaluation integration for red-teaming
- 🏛️ Azure GCC (Government Community Cloud) compatible
- 🔐 PII detection and automatic masking
- 👤 Role-based access control via Entra ID
- 🔒 Encrypted data at rest and in transit
- 🗑️ 30-day conversation data purge policy
newyork/
├── 📂 Constituent-Services-Agent/ # 💬 Citizen Q&A chatbot
│ ├── src/
│ │ ├── agent/ # AI agent components
│ │ ├── api/ # Flask routes
│ │ ├── models/ # Data models
│ │ └── services/ # Knowledge service
│ ├── demo.py # Interactive demo
│ └── requirements.txt
│
├── 📂 Document-Eligibility-Agent/ # 📄 Document processing
│ ├── src/
│ │ ├── agent/ # Processing agents
│ │ ├── api/ # REST endpoints
│ │ ├── models/ # Document models
│ │ └── services/ # OCR, email, storage
│ ├── demo.py
│ └── sample_documents/
│
├── 📂 Emergency-Response-Agent/ # 🚨 Emergency planning
│ ├── src/
│ │ ├── models/ # Emergency models
│ │ ├── orchestration/ # Multi-agent coordinator
│ │ └── services/ # Weather, traffic APIs
│ └── requirements.txt
│
├── 📂 Policy-Compliance-Checker/ # 📋 Compliance checking
│ ├── src/
│ │ ├── models/ # Compliance models
│ │ ├── services/ # Rule engine, parsing
│ │ └── api/ # Flask routes
│ └── requirements.txt
│
├── 📂 Inter-Agency-Knowledge-Hub/ # 🔍 Cross-agency search
│ ├── src/
│ │ ├── models/ # Search models
│ │ ├── services/ # Search, auth services
│ │ └── api/ # Flask routes
│ └── requirements.txt
│
├── 📂 DotNet-Virtual-Citizen-Assistant/ # 🏙️ NYC .NET chatbot
│ ├── VirtualCitizenAgent/ # Main web application
│ │ ├── Controllers/ # MVC and API controllers
│ │ ├── Services/ # Business logic
│ │ ├── Plugins/ # Semantic Kernel plugins
│ │ └── Views/ # Razor views
│ ├── VirtualCitizenAgent.Tests/ # xUnit tests
│ └── AzureSearchUploader/ # Data upload utility
│
├── 📂 Virtual-Citizen-Assistant/ # 🤖 NYC Python chatbot
│ ├── src/
│ │ ├── config/ # Configuration settings
│ │ ├── models/ # Data models
│ │ ├── plugins/ # Semantic Kernel plugins
│ │ └── main.py # Main application
│ ├── test_setup.py # Setup validation
│ ├── test_plugins.py # Plugin tests
│ └── requirements.txt
│
├── 📂 docs/ # 📖 Documentation
│ ├── QUICKSTART.md # Quick start guide
│ ├── EVAL_GUIDE.md # Evaluation guide
│ └── SPEC_TEMPLATE.md # Specification template
│
├── 📂 evaluation/ # 🧪 AI evaluation framework
│ ├── eval_config.py # Evaluation configuration
│ ├── run_evals.py # Run evaluations
│ ├── red_team.yaml # Red team test config
│ └── test_cases.jsonl # Test cases
│
└── 📂 specs/ # 📋 Feature specifications
├── 001-constituent-services-agent/
├── 002-document-eligibility-agent/
├── 003-emergency-response-agent/
├── 004-policy-compliance-checker/
└── 005-inter-agency-knowledge-hub/
# 1️⃣ Clone and navigate
cd newyork
# 2️⃣ Choose an accelerator
cd Constituent-Services-Agent # or any other accelerator
# 3️⃣ Create virtual environment
python -m venv venv
venv\Scripts\activate # Windows
# source venv/bin/activate # Mac/Linux
# 4️⃣ Install dependencies
pip install -r requirements.txt
# 5️⃣ Run demo (mock mode - no Azure required)
python demo.py
# 6️⃣ Run web interface
python -m src.main💡 Mock Mode: All accelerators work without Azure services using mock data for offline development. No API keys required to get started!
| Technology | Purpose | Badge |
|---|---|---|
| Azure AI Foundry | AI development platform |  |
| Foundry IQ | Intelligent document retrieval |  |
| Semantic Kernel | AI orchestration framework |  |
| Azure OpenAI GPT-4o | Language model for Q&A |  |
| Azure Document Intelligence | OCR and document parsing |  |
| Azure AI Search | Vector search with security filters |  |
| Microsoft Graph | Email and document access |  |
| Entra ID | Authentication and authorization |  |
| Flask | Web framework for APIs |  |
| Pydantic | Data validation |  |
| Accelerator | Tests | Status |
|---|---|---|
| Constituent Services Agent | 43 | ✅ All Passing |
| Document Eligibility Agent | 86 | ✅ All Passing |
| Emergency Response Agent | 62 | ✅ All Passing |
| Policy Compliance Checker | 14 | ✅ All Passing |
| Inter-Agency Knowledge Hub | 38 | ✅ All Passing |
| Virtual Citizen Assistant (.NET) | 22 | ✅ All Passing |
| Virtual Citizen Assistant (Python) | 2 | ✅ All Passing |
| Total | 267 | ✅ Production Ready |
- Quality Evaluators: Groundedness, Relevance, Coherence, Fluency
- Safety Evaluators: Content safety, PII detection
- Red Team Tests: Jailbreak, PII extraction, authority spoofing, hallucination
# Run tests for Python accelerators
cd [Accelerator-Directory]
python -m pytest tests/ -v
# Run tests for .NET accelerator
cd DotNet-Virtual-Citizen-Assistant
dotnet test
# Run AI evaluations
python -m shared.evaluation.eval_config| Accelerator | Key Metric | Target | Status |
|---|---|---|---|
| 💬 Constituent Services | Response time | < 5 seconds | ✅ |
| 💬 Constituent Services | Citation accuracy | > 95% | ✅ |
| 📄 Document Eligibility | Processing time | < 2 minutes | ✅ |
| 📄 Document Eligibility | Extraction accuracy | > 95% | ✅ |
| 🚨 Emergency Response | Plan generation | < 5 seconds | ✅ |
| 📋 Policy Compliance | Analysis time | < 30 seconds | ✅ |
| 🔍 Knowledge Hub | Search response | < 3 seconds | ✅ |
- ⚡ Faster answers: Get information about government services instantly
- 🌍 Accessible: Multi-language support, WCAG 2.1 AA compliant
- 📚 Transparent: See sources for all information provided
- 📉 Reduced workload: AI handles routine inquiries, staff focus on complex cases
- ⏱️ Faster processing: Documents processed in minutes, not hours
- 🤝 Better coordination: Cross-agency visibility and emergency planning
- ✅ Compliance: Built-in LOADinG Act and RAISE Act compliance
- 📈 Scalability: Handles high volumes during crises
- 📋 Accountability: Complete audit trails for all AI decisions
For Microsoft Enterprise Users: If you have a Microsoft enterprise account and are having trouble accessing this repository, please see our detailed Collaboration Guide for step-by-step instructions.
Quick Access Steps:
- Ensure your GitHub account has 2FA enabled
- Link your Microsoft enterprise email to your GitHub account
- Request access from the repository owner (@msftsean)
- For detailed instructions, see COLLABORATION.md
We welcome contributions! Please see our Contributing Guidelines for:
- Code standards and best practices
- Pull request process
- Testing requirements
- Security considerations
Quick Start for Contributors:
# Fork and clone the repository
git clone https://github.com/msftsean/ai-hackathon-use-cases.git
# Create a feature branch
git checkout -b feature/your-feature-name
# Make changes and run tests
pytest tests/ -v # Python projects
dotnet test # .NET project
# Submit a pull request- 🚀 Quick Start Guide
- 🤝 Collaboration Guide - For Microsoft enterprise users
- 📝 Contributing Guidelines
- 📋 Feature Specifications
- 🧪 Evaluation Framework
- 📖 Evaluation Guide
- 🔗 Azure AI Foundry Documentation
- 🔗 Semantic Kernel Documentation
- 🔗 Microsoft Accelerators
🏛️ Shaping the Future of Responsible AI in New York State 🗽