Intelligent e-commerce scraping platform powered by LLMs to extract structured product data from dynamic websites without hardcoded selectors.
ScrapLLM combines scraping infrastructure, HTML preprocessing, LLM-based structured extraction, validation, and monitoring into a production-ready pipeline.
ScrapLLM is designed to automate large-scale product data extraction across heterogeneous e-commerce websites.
The system:
- Scrapes product pages from 50+ websites
- Cleans and preprocesses raw HTML to reduce noise
- Uses LLMs to extract structured product fields
- Validates and normalizes outputs
- Monitors performance, latency, and cost across multiple models
| Metric | Result |
|---|---|
| Structured extraction accuracy | 95% |
| Token consumption reduction | 60% |
| API cost reduction | $400/month |
| Response time improvement | 40% |
Traditional scraping systems rely on manual CSS selector mapping per website. This approach:
- Does not scale across many brands
- Breaks when websites update layouts
- Requires continuous maintenance
- Increases operational cost
ScrapLLM eliminates the need for static selectors by leveraging LLMs to dynamically interpret and extract structured product data directly from cleaned HTML.
High-level flow:
URL Ingestion
↓
Scraping Layer (HTML Retrieval)
↓
HTML Cleaning & Preprocessing
↓
LLM-Based Structured Extraction
↓
Output Validation & Normalization
↓
Monitoring & Evaluation
The system is modular, extensible, and designed for multi-model experimentation.
To reduce token usage and cost:
- Removed non-semantic DOM elements
- Extracted product-relevant sections
- Applied intelligent truncation strategies
- Reduced prompt size by 60% without degrading extraction quality
- JSON schema-based prompting
- Output validation layer
- Automatic retry on malformed responses
- Normalization of price, currency, and availability formats
Integrated monitoring to:
- Compare multiple LLM providers
- Track latency and token usage
- Measure cost efficiency
- Evaluate structured field accuracy
This enabled data-driven model selection and performance optimization.
| Layer | Technology |
|---|---|
| Backend | FastAPI |
| AI Orchestration | LangChain |
| Monitoring | Langfuse |
| Frontend | React |
| Containerization | Docker |
| Cloud | GCP |
- Eliminated manual product data entry
- Scaled extraction across 50+ heterogeneous websites
- Reduced API costs through preprocessing optimization
- Improved response latency through structured benchmarking
- Built a reusable architecture adaptable to new brands without selector engineering
Check out the Link
Check out the Link
- Distributed scraping workers
- Queue-based processing architecture
- Extraction confidence scoring
- Hybrid selector + LLM fallback strategy
- Domain-specific fine-tuned extraction model