Overview
Financial institutions were manually monitoring 500+ pages across 15+ comparison sites every day, and traditional scrapers kept breaking whenever site layouts changed. The solution is a self-healing AI platform that uses semantic HTML analysis (81.82% accuracy), adaptive parsing (98% success rate), and automated analytics to collect and analyze competitive data, adapt to site changes in hours, and turn raw data into trend analysis and strategic recommendations.
Achievements
The system achieved 81.82% accuracy in semantic HTML analysis (compared to 63.58% using traditional methods) and maintained a 98% extraction success rate across 500+ daily pages. By implementing an AI-driven self-healing engine, the Mean Time to Repair (MTTR) was slashed from days to minutes, while 40–60 hours of manual data collection were automated weekly per analyst, resulting in a 60–70% reduction in labor costs. Furthermore, the platform reached over 95% schema coverage and integrated automated quality scoring for every extracted financial attribute, ensuring high-fidelity data for strategic decision-making.
Responsibilities
- Designed a self-healing AI architecture with five-layer error prevention (proxy rotation, schema drift detection, and LLM-based fallback).
- Integrated OpenAI API for semantic HTML understanding and multi-dimensional trend analysis (seasonality, anomaly detection).
- Developed a modular data pipeline for extracting 15+ financial attributes with intelligent normalization and deduplication.
- Built an automated insight generation system that transforms raw market data into natural language strategic recommendations.
- Implemented CI/CD for scraping configurations with AI-generated suggestions for rapid adaptation to site changes.
This project was delivered by
Yurii K.
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