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Letting Data Speak, AI Act!
Case Study
AI-Powered Transportation Report Generation System
About the Client
A leading global provider of transportation intelligence solutions that helps people make better commuting and transportation choices. The company is known for creating screens that display the real-time transportation information, and offers transit data solutions for commercial real estate, transportation demand management (TDM), and employers.
Challenge
Scenario: A real estate developer needs a comprehensive transportation analysis for a new 200-unit residential complex. Traditional consulting requires hiring specialized transportation engineers who manually gather data from multiple sources, analyze mobility patterns, research local amenities, and compile 15+ page reports—a process taking weeks and costing thousands of dollars per report.
Traditional Approach: Transportation consultants manually collect data from various APIs, perform site visits, analyze mobility patterns, and spend days formatting reports with executive summaries, detailed analysis sections, and supporting data tables. Each report is essentially built from scratch, with consultants repeatedly performing similar data collection tasks across different projects.
The Real Problem: This manual process creates bottlenecks for developers who need rapid feasibility assessments for multiple potential sites. The high cost and time investment often delays decision-making, while human consultants struggle to maintain consistency across reports and may miss relevant data sources. The lack of standardization also makes it difficult to compare analyses across different locations.
Root Cause: Traditional transportation consulting lacks systematic data orchestration and standardized analytical frameworks. The process treats each report as a unique endeavor rather than leveraging repeatable AI-driven workflows that can consistently gather, analyze, and synthesize transportation data from multiple APIs into professional-grade reports.
Key Results
Report Generation & Quality:
Reduced report generation time from 2-3 weeks to under 1 hour through automated data collection and synthesis
Achieved 100% consistency in report structure and formatting by using sample report templates as structural guides
Eliminated manual data gathering errors by implementing automated API integrations with real-time validation
Cost & Operational Efficiency:
Reduced transportation analysis costs by 80% through AI automation, making feasibility studies accessible for smaller developers
Cut consultant workload by automating routine data collection tasks, allowing focus on high-value strategic analysis
Enabled concurrent analysis of multiple locations, supporting rapid site comparison and selection
Technical & Scalability Achievements:
Implemented serverless AWS architecture supporting unlimited concurrent report generation with zero infrastructure management
Achieved sub-second response latency for all API integrations (Mobility Score, Walk Score, Google Places, Yelp)
Processed location data with 100% accuracy from both street addresses and latitude/longitude coordinates
Enabled real-time progress tracking through Server-Sent Events (SSE) streaming for transparent user experience
Extended Impact: Industry Transformation:
Developer ROI: Enabled rapid feasibility analysis across multiple sites, accelerating project pipeline decisions
Consultant Productivity: Freed transportation engineers from routine data collection to focus on strategic recommendations and complex analysis
Market Access: Made professional transportation analysis accessible to smaller developers previously priced out of consulting services
Solution
The team built a comprehensive, multi-agent AI system with four integrated components orchestrated through AWS Bedrock:
Core Components:
1. Intelligent Agent Orchestration Engine
Main LLM Orchestrator: AWS Bedrock Claude 3.7/4.0 supervisor agent coordinating specialized sub-agents through structured workflows
Phased Execution Logic: Automated progression through data collection → synthesis → validation → report generation phases
Context-Aware Coordination: Maintains session state and context across multiple API calls and agent interactions
Dynamic Task Allocation: Distributes data collection tasks to appropriate sub-agents based on information dependencies
2. Specialized Agent Architecture
Mobility Scores Agent: Migrated from OpenAI prototype to AWS Bedrock, integrates with proprietary Mobility Score API and Walk Score API for transportation accessibility analysis
Business Research Agent: Leverages Google Places and Yelp APIs to gather comprehensive amenity and commercial facility data
Orchestrator Agent – Manages the Mobility Scores Agent and Business Research Agent, while also handling report synthesis and quality validation tasks to ensure completeness and structural alignment with templates.
3. Context-Aware Personalization System
scores_server: Dedicated MCP server exposing mobility_score (MobilityScore API) and walk_score (Walk Score API) tools with robust parameter validation
businesses_server: Specialized MCP server providing google_places (Google Places New API) and yelp (Yelp Fusion) integration with comprehensive business data retrieval
API Orchestration: Centralized management of external API calls with error handling and response validation
Data Normalization: Consistent data structure conversion across different API response formats
4. Automated Report Generation & Delivery System
Template-Driven Structure: Uses uploaded sample reports (PDF) to extract formatting, section ordering, and content structure requirements
Dynamic Content Synthesis: Combines API data into cohesive narrative following transportation planning best practices
Multi-Format Output: Generates both markdown and interactive HTML reports with embedded data visualizations
Version Management: Automated report versioning under reports/{thread_id}/ directory structure
Technologies Used
Component | Technology & Stack | Deployment & Protocol |
Frontend | React, JavaScript, Ant Design | AWS EC2 (via pm2) |
API Layer | Python FastAPI | AWS EC2 (via pm2) |
AI Orchestration | AWS Bedrock (Claude 3.7/4.0) | Managed AI Service via boto3 |
Agent Framework | LangGraph framework | Langgraph framework for agent creation and management |
State management | LangGraph with InMemorySaver | State management for multi-agent workflows |
Data Storage | Local filesystem (reports/{thread_id}/) | Versioned markdown and HTML report storage |
Streaming | FastAPI + Server-Sent Events (SSE) | Real-time progress updates to frontend |
File Processing | PyPDF2 for sample report parsing | Text extraction from uploaded templates |
External APIs | Mobility Score, Walk Score, Google Places, Yelp | HTTPS API integration via MCP servers |
Key Technical Implementation Details
Agentic Architecture:
LangGraph Supervisor: Coordinates phased execution of specialized sub-agents with dependency management
Context Persistence: InMemorySaver maintains conversation state and report progress across agent interactions
Prompt Engineering: Dynamic prompt injection using sample report text to guide structural adherence
API Integration Strategy:
MCP Protocol: Standardized tool-calling interface enabling modular API integration with consistent error handling
Concurrent Data Collection: Parallel execution of mobility and business research agents for optimized performance
Response Validation: Structured JSON validation ensuring data quality before synthesis
Report Generation Workflow:
Input Processing: Parse location data (address/coordinates) and extract sample report structure from PDF
Data Collection: Concurrent API calls through MCP servers gathering mobility scores and business amenities
Synthesis: AI-driven transformation of raw API data into structured report sections matching sample format
Validation: Quality checks ensuring completeness and adherence to transportation planning standards
Output Generation: Markdown creation with optional HTML conversion for interactive presentation
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