Technology / AI / Data

Semantic Search & Content Enrichment

Design and implementation of embedding-based semantic search systems combined with AI-driven content enrichment for product catalogs and enterprise databases.

Challenge

Large catalogs or document repositories with weak descriptions and text-only search engines.
Initial situation: noisy results, difficulty finding related content and poor exploratory search experience.

Solution

Implementation of semantic search engines based on vector embeddings and relational databases.
AI-driven content enrichment processes (text and image analysis) generate consistent semantic metadata.
The system combines exact reference detection with similarity-based retrieval and supports result expansion when assistant response limits are reached.

Impact

Significant improvements in search relevance and content quality.
Users can locate products, documents or entire categories even without exact terminology.
The solution is ready for future search and conversational assistant enhancements.