Self-Healing Fashion Intelligence
Automated trend detection across 100+ data sources
Fashion designers were manually consuming 100+ blogs, news sites, fashion shows, and fast-fashion catalogs daily to build intuition about trending colors, materials, and silhouettes.
The client needed a tool to identify fashion trends and maintain a dynamic, adaptive view of the entire industry. Their existing workflow required designers to manually parse hundreds of unstructured data sources daily—an unsustainable process that couldn't scale. Traditional scrapers broke constantly as websites changed layouts, creating a maintenance nightmare.
Self-Healing Scrapers
LLMs parse website DOM structures and generate extraction logic that automatically adapts when layouts change. Scrapers fix themselves on new layouts without manual intervention.
Human-in-the-Loop Debugging
Chrome extensions enable manual intervention for edge cases. Engineers can debug and refine scraper logic through an intuitive interface when the AI needs guidance.
Multimodal Data Extraction
Vision models (Llama 3.2, LLaVA) extract fashion attributes from images—patterns, materials, silhouettes—that aren't captured in product descriptions.
Auto Trend Identification
Embeddings cluster similar products across sources to surface emerging trends. Groups items by color, silhouette, and material—even when described differently across sites.
DOM Compression
Web pages are large. We strip irrelevant HTML and compress DOM structures before sending to the LLM, keeping extraction accurate while fitting within context limits.
Add and ingest any new data source 30× faster than manually writing scrapers.
Self-healing extraction eliminates weekly scraper maintenance cycles.
Unstructured data converted to queryable, filterable structured format.
LLM-powered trend reports synthesize insights across all sources.
Filter-based exploration lets designers slice data by color, material, brand, silhouette.
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