Problem Statement
Legacy keyword-based search systems fail to handle semantic queries across 10M+ SKU catalogs, resulting in 67% search relevance accuracy, poor product discovery, and suboptimal conversion rates.
Solution Architecture
Deployed multimodal vector search architecture processing product images, descriptions, reviews, and video content through custom-trained embeddings (768-dimensional vectors) optimized for e-commerce semantics. Agentic workflows orchestrate dynamic pricing optimization across 500K+ products using real-time inventory monitoring (2M+ units), competitor analysis, and demand forecasting. Distributed inference infrastructure handles 100K+ queries/second with sub-50ms p95 latency, maintaining 99.97% uptime through multi-region deployment.
Impact Metrics
Search relevance accuracy improved from 67% to 94.3% (41% absolute increase) with vector-based semantic retrieval
Conversion rates increased 40% through AI-driven recommendations processing 2.5M daily user interactions with 23% CTR
Cart abandonment reduced 60% via intelligent inventory and pricing workflows, generating $3.2M annual cost savings
Query throughput scaled to 100K+ QPS with distributed inference, supporting 10x traffic spikes during peak events
Average order value increased 18% through personalized recommendations, while inventory optimization reduced stockouts by 42% and overstock by 38%
Customer service automation handles 78% of inquiries autonomously with 89% satisfaction scores, reducing support costs by $1.8M annually
Technology Stack
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