Semantic Search
Search that ranks results by meaning rather than exact keyword match, using vector embeddings to retrieve semantically-similar content even when no terms overlap.
How it works
Semantic search is the retrieval substrate for modern enterprise knowledge work. A user asks "how did we handle the supplier dispute last year" and the system returns relevant matter files, emails, and meeting notes, even though none contain those exact words. Implementation: documents are chunked and embedded into vector representations; a query is embedded the same way; the vector database returns the top-k closest matches by cosine similarity. Production systems combine semantic search with keyword search (hybrid search) to capture both exact-match precision and semantic recall. Ayoob AI builds semantic search as the retrieval layer for RAG, knowledge-management, and document-intelligence workloads, deployed on the client's own infrastructure.
Related terms
Vector Embedding
A high-dimensional numerical representation of text, image, or other content that places semantically similar items close together in vector space, enabling similarity search and clustering.
Vector Database
A database optimised for storing and querying high-dimensional vector embeddings using approximate nearest-neighbour algorithms, used as the retrieval layer in RAG systems and semantic search.
Hybrid Search
A retrieval architecture that combines keyword search (BM25 or similar) with semantic vector search, blending the two ranked results to capture both exact-match precision and semantic recall.
Retrieval-Augmented Generation (RAG)
An architecture pattern that grounds language model outputs in retrieved documents from a private corpus, reducing hallucination and enabling answers based on the firm's own data rather than the model's training set.
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