Semantic search that finds what users mean, not just what they typed.

Keyword search finds documents that contain the words in your query, it matches strings, not meaning. Semantic search finds documents that are conceptually similar to your query, it understands that 'ways to reduce employee turnover' is related to 'retention strategies' and 'engagement initiatives', even though the words don't overlap. Semantic search uses vector embeddings: your query and your documents are converted to high-dimensional vectors, and retrieval finds the vectors most similar to the query vector. The result: users find relevant content when they describe what they want in their own words.

Hybrid search combines semantic vector retrieval with traditional BM25 keyword search and merges the results (typically using reciprocal rank fusion or a re-ranker). Pure semantic search is great for intent matching but can miss exact terms, product codes, proper nouns, technical identifiers, and precise specifications. Pure keyword search is great for exact matches but misses conceptual relevance. Hybrid search outperforms either alone for most real-world search use cases: e-commerce product search, knowledge base Q&A, enterprise document search, and developer documentation. We implement hybrid search as the default for most production systems.

Semantic search retrieves relevant results and returns them as a list for the user to choose from, the user selects what they want from the ranked results. A RAG pipeline retrieves relevant content and passes it to a language model, which synthesises the retrieved content into a single answer, the user gets a direct answer, not a list of results. Semantic search is the right choice for search interfaces. RAG is the right choice for question-answering interfaces. The vector retrieval layer is shared between both, we build semantic search as a standalone product and as the retrieval layer inside RAG systems.

Embedding model selection depends on your content type, query patterns, and cost constraints. For general-purpose text: OpenAI text-embedding-3-small (cost-efficient, high quality) or text-embedding-3-large (higher accuracy, higher cost). For multilingual content: multilingual-e5-large or multilingual models from Cohere. For domain-specific content (medical, legal, technical): fine-tuned domain-specific models significantly outperform general models on domain vocabulary. We select and evaluate the embedding model against your specific content before production deployment.

Integrating semantic search into an existing product (replacing or augmenting an existing search feature) typically runs $20,000-$45,000. A standalone semantic search application with custom UI, hybrid retrieval, and re-ranking runs $30,000-$65,000. Enterprise search across multiple content sources with access control and monitoring runs $50,000-$100,000. Embedding and retrieval infrastructure costs at production volume depend on query load and index size, most systems run on $200-$1,500/month.

Integrating semantic search into an existing product takes 6-10 weeks: 1 week for scoping and model selection, 2 weeks for embedding pipeline and index setup, 3-5 weeks for integration and QA, and 1-2 weeks for tuning against real query data. A standalone application with a custom UI, hybrid retrieval, and re-ranking typically takes 10-14 weeks. Enterprise internal search across multiple sources runs 14-18 weeks depending on the number of connectors and access control complexity.

Yes. Most of our semantic search projects are integrations, not greenfield builds. We replace or augment existing keyword search with a semantic retrieval layer that sits behind your current search API or UI. The integration approach depends on your stack: we expose a REST or GraphQL search endpoint that your frontend queries, keeping your application layer unchanged while the retrieval layer shifts to vector-based. We also integrate with platforms like Shopify, Salesforce, Zendesk, and Confluence through their native APIs.