Nationwide Service Discovery at Scale: AI-Powered Web Crawling Framework for a Leading Nonprofit
Challenges
1- Inconsistent site structures required adaptive scraping.
2- JavaScript content blocked traditional scrapers.
3- API rate limits impacted data throughput.
4- Complex structuring of 30+ data fields from raw content.
5- Real-time processing needed at national scale.
Solutions
1- Scoped scraping by state, city, and service type.
2- Brave and Google Places APIs for accurate URL discovery.
3- Headless, JavaScript-capable scraping engine.
4- OpenAI used for NLP-based data structuring.
5- Serverless architecture with Azure Functions.
6- Scalable storage via Azure Blob.
Results
1- Processed 40,000+ service queries nationwide.
2- Extracted data from 500+ unique websites.
3- Captured 30+ structured attributes per listing.
4- Achieved 2x faster performance than traditional methods.
5- Enabled real-time access with advanced filtering.
6- Delivered a scalable, future-ready system.
Our client is a national nonprofit organization dedicated to providing dignified, community-based transportation services to senior adults and individuals with mobility challenges. Their mission is to ensure accessible, affordable, and reliable mobility options across the U.S., empowering underserved populations with independence and connection.
The organization lacked a centralized, searchable transportation service database across the United States. Manual data collection from over 500 websites was inefficient, error-prone, and unsustainable—especially with 40,000+ service queries and the need for over 30 structured data points per entry.
We developed a scalable, AI-powered, cloud-integrated web crawler framework. The solution automated data discovery, structured extraction, and storage—leveraging headless scraping, OpenAI for content structuring, search APIs for URL discovery, and Azure for processing and storage scalability.
- Inconsistent site structures demanded adaptive scraping.
- JavaScript content blocked traditional scrapers.
- Bing, Brave, and OpenAI APIs imposed volume and speed limitations.
- Need for accurate structuring of 30+ service fields from messy data.
- Real-time processing of large-scale datasets.
- Scoped Query Generation – Users define scraping by state/city and service type.
- Dual Search Strategy – Google Places API + Brave for precise URL discovery.
- Dynamic Scraping Engine – JavaScript-capable scraping via headless browsers.
- AI Structuring – OpenAI transforms raw content into structured fields.
- Serverless Processing – Azure Functions ensure scale and performance.
- Centralized Cloud Storage – Azure Blob Storage handles high-volume structured data.
High Volume Processing
Successfully handled over 40,000 transportation service queries across the U.S.
Extensive Data Coverage
Extracted and structured data from 500+ unique websites.
Comprehensive Data Fields
Captured 30+ structured service attributes per listing for accurate filtering and insights.
Improved Efficiency
Achieved 2x faster performance compared to traditional scraping approaches.
Real-Time, Targeted Access
Enabled real-time access to region-specific services with advanced filtering capabilities.
Scalable and Sustainable Architecture
Delivered a fully maintainable, extensible system for ongoing data updates and national expansion.
