A procurement technician types «incidents with suppliers from Germany» into the company's internal search engine and the system returns zero results. The report they need exists; it is called «Claims against Central European vendors». The words are different; the meaning is identical. That mismatch, multiplied across dozens of employees and hundreds of queries a day, translates into wasted time, decisions made without the right information, and accumulated frustration. Semantic search solves exactly that problem: it understands the meaning of the question and returns the relevant document even when no word matches.
What semantic search is and how it differs from keyword search
Traditional search, known as lexical search or keyword search, works by matching exact terms. If the document says «parental leave» and the query says «maternity permission», the result is empty even though both texts discuss the same topic. Semantic search, by contrast, converts both the document and the query into mathematical representations—called embeddings or vector representations—that capture meaning in a multidimensional space. Two phrases with the same sense end up «close» in that space even if they share no word.
The practical difference is enormous. In a business environment where documents have inconsistent titles, internal acronyms, typographical errors and terminology that varies between departments, lexical search fails frequently. Semantic search handles that variability naturally because it works on the concept, not on the character string.
How semantic search works: the role of embeddings and vector databases
The process has three well-defined stages:
- Indexing: each document, paragraph or text fragment is transformed into a numerical vector using a language model (for example, one from the family of multilingual embedding models). The vector captures the contextual meaning of the text.
- Storage: the vectors are stored in a vector database (Pinecone, Weaviate, Qdrant, pgvector on PostgreSQL, among others). These databases are optimised for similarity search in an efficient manner, even across millions of documents.
- Retrieval: when the user submits a query, it is converted on the fly into a vector using the same model. The system calculates the cosine distance between that vector and the stored ones, and returns the most semantically close fragments.
The result can be served directly as a list of relevant documents, or fed into a retrieval-augmented generation (RAG) system, where a language model drafts a natural-language answer based on the retrieved fragments. At Summum IA we deploy both modalities depending on the use case: see our semantic search service for enterprises here.
The market in 2025–2026: why now
The market context explains the acceleration we are witnessing. The global enterprise semantic search market was valued at 4.08 billion dollars in 2025 and is projected to reach 13.56 billion by 2034, with a compound annual growth rate of 14.2 %. The vector database market, meanwhile, will grow from 2.65 billion dollars in 2025 to 8.95 billion in 2030 (CAGR of 27.5 %), according to MarketsandMarkets data published in 2025.
More relevant for a company evaluating the investment: the majority of employees in knowledge-intensive sectors rely daily on enterprise search systems to retrieve information, according to various industry market studies. When that system fails or returns irrelevant results, the productivity impact is immediate and measurable.
Real use cases in the mid-sized enterprise
Semantic search is not only technology for large corporations. A company of 50 or 150 people accumulates hundreds of documents in SharePoint, Confluence, Google Drive or simply in server folders that nobody organises properly. This is where the value is most evident:
Internal documentation management
Procedures, quality protocols, product manuals, internal regulations and meeting minutes. A new employee can find in seconds the current returns policy even without knowing what the document is called, which department published it or on what date.
Customer service and technical support
Support agents search for solutions in knowledge bases using customer language («the appliance does not heat properly») that does not match the technical terminology in the manual («fault in NTC heating element»). Semantic search bridges that gap without the need for manual synonym lists.
Due diligence and contract analysis
In M&A operations, compliance or legal review, locating clauses by their content—not by their article number—saves days of work. A firm working with semantic search across its document repository can answer questions such as «in which contracts does a territorial exclusivity clause appear?» in minutes.
Product knowledge and catalogue
Sales teams search for specifications, comparisons and case studies by functional description («moisture-resistant product for outdoor use») rather than by catalogue reference. The system returns the relevant data sheets even when the query uses none of the catalogue's terms.
Semantic search versus other technologies: comparison table
| Technology | How it works | Strength | Limitation | When to use it |
|---|---|---|---|---|
| Lexical search (BM25) | Exact term matching weighted by frequency | Very fast, no GPU required, easy to deploy | Fails with synonyms, errors and paraphrases | Small corpora, stable terminology |
| Semantic search (vector) | Embedding similarity in vector space | Understands meaning, handles linguistic variation | Requires an embeddings model and vector infrastructure | Extensive documentation, natural-language queries |
| Hybrid search | Combines BM25 + vector with reranking | Better precision in most domains | Greater architectural complexity | Demanding production environments |
| RAG (search + generation) | Retrieves relevant fragments and generates a natural-language answer | Answers questions rather than just listing documents | Requires a language model in the generative layer | Internal assistants, copilots, Q&A over documentation |
In practice, the optimal architecture for most mid-sized enterprises in 2025–2026 is hybrid search combined with a reranking model: it retrieves candidates with BM25 for speed, then refines the ranking with vector similarity. If the goal is to answer questions rather than list documents, a RAG layer is added on top of the result.
Technical and data requirements for deploying semantic search
Before embarking on a deployment, three dimensions must be assessed:
Quality and structure of the document corpus
A semantic search system does not improve poorly organised documentation; it indexes it as-is. If documents lack metadata (date, department, version), the results will not be able to distinguish a current policy from an obsolete one. The first step is always a minimal document audit.
Choice of embeddings model
For Spanish-language content, it is essential to use a model trained on multilingual or Spanish-specific data. Models such as multilingual-e5, LaBSE or the multilingual variants of the sentence-transformers family deliver good results. If the domain is highly specific—legal, medical, industrial—fine-tuning on a proprietary corpus may be worthwhile.
Infrastructure and data governance
The vector database can be deployed in the cloud (the fastest option to get started) or on own servers (the preferred option when documents contain confidential information or when the General Data Protection Regulation requires data to be kept within European territory). Governance must define who can access which documents through the search engine, replicating the permissions already in place in the source system.
Privacy, GDPR and semantic search: what you need to know
When the indexed documents contain personal data—employee files, customer histories, internal communications—deploying a semantic search system constitutes data processing under the GDPR (EU Regulation 2016/679). This entails, among other obligations:
- Including the system in the record of processing activities.
- Assessing whether a Data Protection Impact Assessment (DPIA) is required, especially if sensitive data are indexed (health, trade union data).
- Ensuring that the search engine's access permissions respect the original authorisations: if an employee cannot view an HR file in the source system, they must not be able to retrieve it through the search engine either.
- If an external embeddings provider (third-party API) is used, verifying the data processing terms and whether a Data Processing Agreement is required.
At Summum IA we accompany the technical deployment with a review of these compliance aspects, coordinating with the GDPR consultancy team when necessary.
How semantic search integrates with the rest of the AI stack
Semantic search is not a standalone system; it is the retrieval component that feeds the others. An enterprise copilot needs to retrieve relevant context before generating a response. An autonomous agent needs to search the documentation to complete a task. A customer service chatbot needs to find the right policy before replying to the user.
For this reason, in the AI architecture of a mid-sized company, semantic search is often the most cost-effective component to deploy first: it lays the foundations for the rest of the use cases to work well. Without precise retrieval, natural language generation hallucinates; with precise retrieval, generative models work on real facts from the company itself.
Frequently asked questions
Does semantic search work well in Spanish?
Yes, provided that an embeddings model trained in Spanish or multilingually is used. Models such as multilingual-e5-large, LaBSE or paraphrase-multilingual-mpnet-base-v2 deliver quality comparable to English for queries in Spanish. Performance can be further improved with fine-tuning on sector-specific corpora (legal, industrial, healthcare).
Can I deploy semantic search without sending my documents to the cloud?
Yes. Both the embeddings model and the vector database can run on own servers or in a private cloud. Tools such as Qdrant, Weaviate or pgvector have self-hosted versions. This option is recommended when documents contain confidential information, personal data or trade secrets that cannot leave the organisation's perimeter.
How long does it take to deploy a semantic search system?
A first working prototype—with a bounded corpus and a basic interface—can be ready in a matter of weeks. A production deployment, with integration into existing systems, permission management, monitoring and model refinement, typically requires between two and four months, depending on document volume and data governance complexity. At Summum IA we define the scope in an advisory session before committing to timelines.
How does semantic search differ from an AI chatbot?
They are different layers of the same architecture. Semantic search is the retrieval component: it locates the relevant documents or fragments. The chatbot or copilot is the generation component: it drafts a natural-language answer. In a well-designed RAG architecture, the chatbot never invents information because it answers based solely on the fragments that semantic search has previously retrieved. Without quality semantic search, the chatbot makes errors; with it, it works on real facts from your company.