AI search that understands meaning without losing exact matches.

Semantic search finds content that is conceptually related to a query even when the wording is different. It normally represents the query and searchable content as embeddings, retrieves similar candidates, and may rerank them. That can help with synonyms, natural-language descriptions, related concepts, and long queries. It does not automatically understand your commercial priorities, permissions, current inventory, exact identifiers, or what your users consider a good result.

Keyword search is strong when the words matter: a product code, legal clause, person, error message, model number, or exact phrase. Semantic search is useful when meaning matters more than shared wording. Most real products need both behaviours. The question is not which method is more advanced. It is which signal should control each query group, field, filter, and position in the result list.

Hybrid search runs lexical and vector retrieval together and combines their candidates before optional reranking. It can preserve exact matches while recovering relevant results for paraphrases and descriptive queries. A production design may also use filters, field boosts, popularity, freshness, availability, permissions, merchandising rules, or domain-specific signals. The blend should be evaluated on real queries rather than fixed by a generic weight.

Search returns ranked products, documents, records, or passages for the user to inspect and choose. Retrieval-augmented generation, or RAG, retrieves evidence and gives it to a language model to compose an answer. Use search when source choice, comparison, browsing, or direct navigation matters. Use RAG when the product must synthesize an answer from approved evidence. Some experiences offer both, but each needs separate acceptance criteria.

It can if vector similarity replaces rather than complements lexical search and structured filtering. Exact identifiers, names, quoted phrases, categories, prices, dates, stock, geography, and access attributes often need deterministic handling. We identify those protected query and field behaviours, keep the relevant keyword or filter path, and test them as regressions when semantic retrieval or reranking changes.

We start with representative queries and expected results or relevance grades from people who understand the product or content. The evaluation can include exact-match success, useful results near the top, zero-result behaviour, filtered queries, protected access cases, segment performance, latency, and cost. Production signals such as reformulation, result clicks, source opens, conversions, abandonment, and explicit feedback add evidence, but none is a complete definition of relevance on its own.

Yes, when the source identity and application design expose dependable access data. Restricted candidates must be removed before their content is returned or passed to another model. The design must reflect changed and revoked access, tenant boundaries, group membership, and source-system rules. Positive and negative permission cases belong in the same regression set as relevance queries.

Yes. The first phase inspects the current search engine, index schema, query API, interface, content or catalogue, analytics, filters, business rules, traffic, and deployment constraints. The best change may be query tuning, synonym or field work, a managed feature, an additional vector index, reranking, or a replacement service behind the current interface. A visible rebuild is not assumed.

Choose the smallest platform that supports the required content, filters, ranking controls, vector and lexical retrieval, permissions, analytics, latency, deployment, operating model, and commercial constraints. Existing managed products are often the sensible answer. A custom layer is justified when the product needs specialised ingestion, ranking, identity, workflow, interface, or control that the selected platform cannot provide cleanly.

Not necessarily. PostgreSQL with pgvector, Elasticsearch, OpenSearch, Azure AI Search, and other platforms can add vector retrieval without creating a separate source of truth. A dedicated vector database may be justified when the measured workload needs different scale, filtering, tenancy, latency, hosting, or operational behaviour. We compare it against the existing platform using the same queries, content, filters, relevance judgements, update tests, latency, and cost before recommending another data system.

We need access to a representative searchable set, the current index or source structure, and examples of queries that succeed, fail, or return the wrong order. Someone on your team must be able to judge which results are useful, which exact behaviours must be preserved, and which business or access rules affect ranking. Perfect analytics are not required, but invented test queries are a weak substitute for real search behaviour.

Every RaftLabs project starts at $9,500. The first paid phase may be a search audit, relevance evaluation, platform comparison, architecture proof, targeted repair, or one bounded production search path. Timing depends on the current system, content condition, index size, query evidence, filters, permissions, interface, integration, traffic, latency, evaluation depth, and operating requirements. The result, acceptance criteria, exclusions, price, and schedule are written before work begins.

Project-specific code, index and ingestion configuration, query and relevance tests, analytics definitions, operating notes, and prepared data remain under client control. Client-controlled cloud, search, source, identity, and analytics accounts are used where practical and provider terms allow. Third-party platforms and open-source components retain their own licences. The handover names how the index changes, how relevance is tested, and who owns the next release.