Semantic Match

This documentation page is currently being prepared. Technical details and usage examples will be available here very soon.

AIP-103+ Semantic Neural Matching

The ProcureOS matching engine leverages advanced Large Language Models (LLMs) to understand the technical nuances of every RFQ. Unlike traditional SQL keyword searches, our system analyzes the 'intent' and 'technical specifications' behind the text.

Vector Embeddings & Cosine Similarity

Every product catalog entry and incoming RFQ is converted into a 1536-dimensional vector using our neural encoder. These vectors are then compared in our specialized vector database. A match is triggered when the cosine similarity score exceeds the threshold of 0.85, ensuring extremely high precision.

Real-time Infrastructure

Average match time is 450ms across 12 global nodes, powered by our distributed indexing architecture.