Semantic searching

Search AI

The Semantic Searching system revolutionizes information retrieval by understanding the meaning and intent behind search queries rather than relying solely on keyword matching. Using dense embeddings and vector databases, the system creates semantic representations of documents and queries, enabling retrieval-augmented LLM capabilities that deliver highly relevant results. This approach enables smarter enterprise knowledge discovery, allowing users to find information using natural language queries and discover related content based on semantic similarity rather than exact text matches.

Completed20246 months

Project Overview

Client:Enterprise Knowledge Systems
Duration:6 months
Team Size:8 developers
Status:Completed

Technologies Used

semantic searchdense embeddingsvector databaseretrieval-augmented LLMknowledge retrieval

Key Features

  • Natural language query understanding
  • Semantic similarity matching
  • Vector-based document retrieval
  • Knowledge graph integration
  • Multi-modal search support
  • Real-time indexing and search

Challenges Overcome

  • Handling ambiguous queries
  • Scaling vector search to large datasets
  • Maintaining search relevance
  • Balancing precision and recall

Results Achieved

  • 60% improvement in search relevance
  • 50% reduction in search time
  • 40% increase in successful information retrieval
  • 85% user satisfaction with search results