Product matching is fundamentally different from generic semantic search. Many enterprise use cases require exact matches, brand-aware substitutions, or precise handling of units, quantities, and multi-attribute cues. These constraints demand AI systems that go beyond vector similarity to incorporate query understanding, structured attributes, and domain-specific logic.

This session presents a practical multi-stage design for high-precision product matching, combining keyword retrieval, dense embeddings trained with contrastive learning and negative sampling, machine-learned ranking layers, and LLM/GPT models for reasoning, disambiguation, and validation. We’ll explore how smart human-in-the-loop (HITL) workflows serve as targeted feedback mechanisms for ambiguous cases and continuous model refinement. Techniques for handling complex queries, reducing false positives, and improving ranking robustness will also be compared.

The talk concludes with insights from a real enterprise product-matching implementation, demonstrating how modern AI methods can deliver scalable, accurate retrieval-ranking pipelines for nuanced, domain-specific search problems.