Users searching your product catalogue or help centre and not finding what they're looking for -- even though the content exists?
Search that fails on synonyms, related terms, and natural language descriptions of what users want?
Semantic Search Development
Keyword search returns pages that contain the words you typed. Semantic search returns results that answer your question -- even when the exact words don't match.
We build semantic search systems that understand what users mean, not just what they typed. Product search that finds relevant items when customers describe what they want. Knowledge base search that surfaces the right answer rather than a list of pages. Internal search across documents, wikis, and data that retrieves by meaning.
Semantic search powered by vector embeddings and meaning-based retrieval
Hybrid search (semantic + keyword BM25) for higher precision across diverse queries
Re-ranking for precision above and beyond initial retrieval
Integration with your existing product catalogue, knowledge base, or document store
RaftLabs builds semantic search systems that retrieve by meaning rather than keyword matching. We implement vector-based semantic search, hybrid retrieval combining semantic and BM25 keyword search, re-ranking for precision, and AI-powered search interfaces for product catalogues, knowledge bases, internal document stores, and e-commerce platforms. Semantic search is the retrieval layer for RAG systems, help centres, product discovery, and internal enterprise search where keyword matching produces poor results.
Search that understands what users mean
The cost of poor search is measurable: failed searches, users leaving, support tickets from people who couldn't find the answer, and lower conversion rates on product catalogues where users couldn't find what they were looking for.
Good semantic search eliminates most of those failures.
What we build
E-commerce and product search
Product search that understands natural language queries and buyer intent: "comfortable running shoes for flat feet" finds the right category even without keyword overlap. Attribute-aware semantic search that combines visual similarity, description similarity, and structured attribute matching. Personalised ranking based on user history. Faceted filtering that works alongside semantic ranking. Merchandising controls for promotions and inventory management. The search that converts browsers into buyers.
Knowledge base and help centre search
Search for customer-facing knowledge bases and help centres that surfaces the answer rather than a list of articles. Users typing questions in natural language find the most relevant article -- even when their vocabulary doesn't match the article's title. Deflects support tickets by helping users self-serve. Integration with your existing help centre platform (Zendesk, Intercom, Freshdesk) or as a standalone search layer.
Enterprise internal search
Unified search across your organisation's internal content: Confluence, Notion, SharePoint, Google Drive, Slack, and internal databases. One search interface that retrieves from all sources, ranked by relevance to the query. Access control enforced at retrieval -- users see only content they have permission to access. Reduces time-to-information for teams dealing with knowledge fragmented across multiple tools.
Developer and documentation search
Semantic search for technical documentation, API references, and code repositories. Finds relevant documentation when developers describe what they're trying to do, rather than the exact function name. Code-aware retrieval that understands programming concepts and returns relevant code examples alongside documentation. Integration with developer tools and documentation platforms.
Hybrid retrieval and re-ranking
Production-grade hybrid retrieval combining semantic vector search with BM25 keyword search, merged via reciprocal rank fusion. Re-ranking pass using a cross-encoder or LLM judge to reorder the initial retrieval by precise relevance to the query. Query expansion and reformulation for queries that retrieve poorly on first attempt. Retrieval evaluation framework measuring Recall@K, Precision@K, and MRR against a representative query set. The retrieval layer that makes semantic search accurate enough for production.
Search analytics and optimisation
Search performance monitoring: query volume, zero-result rate, click-through rate per result position, and failed search patterns. Dashboards for understanding what users search for and where retrieval fails. Automated alerting for quality degradation. A/B testing framework for retrieval configurations. Ongoing optimisation based on real query patterns -- because production search behaviour rarely matches what you tested during development.
Users not finding what they're searching for?
Tell us your content types, query patterns, and what good search looks like for your use case. We'll design the retrieval system.
Related services
Vector Database Development -- the vector storage and retrieval infrastructure
RAG Pipeline Development -- question-answering that retrieves and synthesises
AI Knowledge Management -- broader knowledge retrieval and management systems
NLP Development -- natural language processing for text understanding
AI Development -- the full AI development capability
Frequently asked questions
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.