Architecting a semantic AI product search engine powering real-time vector queries across millions of catalog SKU listings.
A fast-growing e-commerce platform needed product search that understood natural language, not just keyword matches. Their existing Elasticsearch setup couldn't handle semantic similarity at catalog scale — returning irrelevant results and driving a 28% cart abandonment rate.
We designed a hybrid retrieval pipeline combining dense vector search (pgvector) with sparse BM25 scoring, orchestrated via LangChain. Product embeddings were generated offline using a fine-tuned sentence-transformer model and stored in a purpose-built vector index. The Next.js frontend streamed results progressively as the ranking model scored candidates in real time. FastAPI served as the inference gateway with request batching and connection pooling to keep latency flat under load.
The system sustained sub-100ms end-to-end query latency across a 4 million SKU catalog with a 10x improvement in semantic relevance scores compared to the prior keyword search baseline. Cart abandonment attributable to poor search results dropped significantly after rollout.
ⓘ This is a concept and illustrative case study. It represents Mehrium's engineering design approach and capability depth — not a verified or named client engagement. No client relationship or endorsement is implied.
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