Semantic Search
Semantic search finds records and content by meaning rather than exact keywords, using vector embeddings to match intent.
What Is Semantic Search?
Semantic search retrieves information based on the *meaning* behind a query rather than literal keyword matches. Where traditional search needs the exact words to line up, semantic search understands that "renewal at risk" and "customer likely to churn" point to the same concept and surfaces both.
How It Works
The process turns text into vector embeddings: numerical representations that place similar meanings close together in a high-dimensional space. Documents and records are chunked, converted into embeddings, and stored in a vector database. At query time, your search is embedded the same way, and the system returns the records whose vectors are mathematically nearest, ranked by semantic similarity instead of word overlap.
Semantic Search in Salesforce (and GPTfy BYOM)
Inside Salesforce, semantic search lets you query across cases, knowledge articles, opportunities, and notes by intent. Salesforce's native path runs this through Data Cloud's vector index. GPTfy takes a Bring Your Own Model (BYOM) approach instead: it can ground your chosen LLM (Claude, GPT, Gemini) on Salesforce records without requiring Data Cloud, applying PII masking so sensitive fields never leave the org unprotected.
Concrete example: A support agent types "billing dispute on enterprise account." Semantic search retrieves the three most relevant past cases and a knowledge article, even though none contain that exact phrase. GPTfy then feeds those grounded results to the LLM, which drafts a resolution the agent can review, all inside the Salesforce record.
FAQ
How is semantic search different from keyword search? Keyword search matches exact terms; semantic search matches meaning using vector embeddings, so synonyms and paraphrases still return the right results.
Does semantic search in Salesforce require Data Cloud? Salesforce's native vector search uses Data Cloud, but GPTfy's BYOM model can deliver grounded, record-level semantic retrieval without it.
Is my Salesforce data safe during semantic search? With GPTfy, PII masking and in-org processing keep sensitive data protected before anything reaches an external LLM.
Related terms
Browse all terms- Vector DatabaseA database optimized for storing and querying high-dimensional vectors (embeddings) — the storage layer that makes semantic search and RAG fast at scale.
- RAG (Retrieval-Augmented Generation)An LLM is given relevant retrieved documents as context before generating a response — grounding outputs in your specific data, not just the model's training.
- BYOM (Bring Your Own Model)An architecture letting enterprises plug their preferred LLM (Claude, GPT-4, Gemini, Llama) into Salesforce instead of being locked to the vendor's default.
See it in your Salesforce org
See Semantic Search running in GPTfy
Book 30 minutes with a GPTfy engineer to see how Semantic Search actually works inside a Salesforce org like yours.
Book a demo