Multi-modal component search and procurement system

A system for component search and procurement comprising:

• an application server comprising one or more processors and a non-transitory memory;
• a multi-modal query processor, executing on the one or more processors of the application server and configured to receive and normalize input data selected from the group consisting of part numbers, keywords, partial information descriptors, natural language descriptions, and Bill of Materials (BOM) files;
• a classifier, executing on the one or more processors of the application server and in communication with a vector database, configured to implement a dual-threshold semantic matching pipeline by:
• converting a user query into a query embedding vector using a transformer-based embedding model;
• querying the vector database to compute similarity metrics between the query embedding vector and a plurality of precomputed category embedding vectors stored therein, wherein each category embedding vector is derived from a structured JSON schema defining a canonical product category;
• based on the similarity metrics relative to a first similarity threshold and a second similarity threshold lower than the first similarity threshold, classifying a query match and triggering a corresponding action while suppressing the others from the group consisting of:
• a direct retrieval of a matched canonical product category when a similarity metric exceeds the first threshold;
• generation of a disambiguation prompt via a conversational agent when the similarity metric is between the first similarity threshold and the second similarity threshold;
• and
• triggering a query reset protocol when the similarity metric is below the second threshold;
• a dynamic interface subsystem, comprising:
• the conversational agent configured to iteratively refine search parameters through context-aware dialogue sequences based on the output from the classifier, and
• a synchronized selection panel configured to display real-time updates of product taxonomies corresponding to query refinements;
• and
• a UI synchronization module, executing on the one or more processors of the application server, configured to maintain bidirectional state synchronization between the conversational agent and the synchronized selection panel, and to update their respective displays within a configurable latency bound (t) in response to changes in matched categories or attributes;
• a recommendation processor executing on the one or more processors of the application server, configured to employ weighted ranking algorithms that prioritize results based on a combination of user-specific parameters including historical procurement patterns, geographic constraints, and organizational purchasing policies;
• and
• a product database architecture, comprising:
• a product datastore storing structured component metadata;
• an inventory monitoring service configured to execute availability-aware inventory queries through API connections to supplier systems only during execution of direct retrieval of the matched canonical product category when the similarity metric exceeds the first threshold;
• wherein the vector database and the product datastore are physically distinct from the application server and accessed via network interfaces.