When your ERP or intranet search returns zero results because an employee typed 'sick leave' and the document says 'temporary incapacity', you have a search problem, not a filing problem. Semantic search understands the meaning of a query and finds the right document even when the exact words do not match. Built for SMEs and mid-market companies with 10 to 250 employees that struggle to locate accumulated internal documentation.
A classic keyword search engine matches strings of text: if the exact term is not in the document, there is no result. Semantic search works in a radically different way: it converts each text fragment into a mathematical representation of its meaning (an embedding) and, when the user submits a query, it compares the meaning of that question against all indexed fragments, returning the most relevant ones even if they share not a single word. In practice, a query like 'what procedure do we follow when a supplier delivers late?' finds the delivery penalty protocol even if the document contains neither 'procedure' nor 'delivers late'.
At Summum IA we design and deploy the semantic search engine on your company's real documentation: quality manuals, contracts, operational procedures, quotations, knowledge bases, email archives and meeting transcripts. The vector index is updated incrementally as new documents arrive, so the search engine grows with your company without manual intervention. For repositories with confidential documentation — employment contracts, health data, legal files — we offer on-premise deployment, with the embedding model running on your own servers and no data leaving your network.
Semantic search is not just a faster search engine: it is the foundation on which more advanced information retrieval systems are built for generative AI (RAG), autonomous agents and corporate copilots. A well-built semantic index today is the infrastructure you need so that tomorrow an AI assistant can answer questions about your operations using your own verifiable data, without hallucinating and without relying on models trained on public information. Summum IA handles the entire chain: indexing, embedding model fine-tuning, end-user search panel and integration with the applications your team already uses.
We identify where your knowledge lives: SharePoint, Google Drive, network drives, ERP, document management systems or proprietary knowledge bases. We define which collections deliver the most value for the search engine — procedures, contracts, internal FAQs, historical proposals — and prioritise indexing order based on impact on daily productivity.
We split documents into optimally sized chunks, generate embeddings using the model best suited to your company's language and domain, and store them in a high-speed vector index (Qdrant, Weaviate or equivalent). We apply classification metadata so the search engine can filter by document type, date, department or confidentiality level.
We test the engine with real queries from your team, measure result relevance and adjust the reranking model. We deliver a web search panel or an extension that integrates the search engine into the tools your team already uses: SharePoint, Teams, intranet or internal portal. The interface returns the relevant fragment with its source and a link to the original document.
We activate the incremental indexing pipeline so that new or modified documents are indexed automatically. We configure a usage analytics panel — most frequent queries, most retrieved documents, searches with no result — to identify documentation gaps. We review performance periodically and update the embedding model as your company's terminology evolves.
The operational detail: what we deliver as part of the work and what we keep alive afterwards.
Vector index over your document sources
Ingestion and chunking of SharePoint, Google Drive, network drives, ERP, document management systems or proprietary knowledge bases, with automatic incremental updates.
End-user search panel
Natural-language search interface accessible from the browser or embedded in Teams, with enriched results: relevant fragment, source, link to the original document and filters by type or date.
Reranking and relevance control
Result reordering layer that combines semantic similarity with business signals — recency, classification, team ratings — to surface the most useful document first.
On-premise deployment available
For confidential documentation, the embedding model and vector index are installed on your own infrastructure. No data leaves your network; performance is comparable to cloud deployment.
Usage analytics and gap detection
Dashboard of zero-result queries, most searched terms and most retrieved documents so the knowledge team can identify missing or outdated content.
Ready for RAG and AI agents
The generated vector index is compatible with RAG architectures and autonomous agents: reuse the same infrastructure for future generative AI projects without rebuilding from scratch.
Semantic search is the central component of Summum's documentary AI projects: it feeds the RAG systems built by Summum IA, integrates with the ERPs managed by Summum Sistemas, and can be governed with the data policies defined by Summum Consultoría.
Build on top of the semantic index a RAG system that answers questions in natural language with verifiable citations from your own documents.
View service → iaAutomatically classify documents before indexing so the search engine distinguishes contracts, procedures, reports and invoices without manual configuration.
View service → sistemasSummum Sistemas complements semantic search with Business Intelligence layers over the structured data in your ERP, covering both unstructured knowledge and analytical data.
View service →A full-text search engine indexes words and finds documents that contain them. If the query and the document use different vocabulary — synonyms, abbreviations, technical terms — there is no match and the result is empty or irrelevant. Semantic search converts both the query and the document into numerical vectors that represent meaning: the distance between vectors measures conceptual relevance, not literal match. In practice, a semantic search engine finds the 'temporary incapacity' manual when an employee asks about 'sick leave', and returns the penalty clause of a contract when a sales rep asks 'what happens to the client if they cancel late?'.
There is no minimum document threshold, but the return is more visible the larger the repository. Companies with more than 500 active documents and teams that frequently look up internal information — procedures, contracts, historical proposals, technical data sheets — notice the benefit from day one. For smaller companies with highly structured documentation, semantic search may be excessive; in that case, a RAG system built on a well-maintained knowledge base may be the most efficient option.
It depends on the deployment model. In the cloud option, text fragments are sent to an external embedding service (such as those of the main AI providers) to generate the vectors. In the on-premise option, the embedding model runs on your own servers: no text fragment leaves your network. This second option is recommended for documentation containing sensitive personal data, trade secrets or information subject to contractual confidentiality obligations.
Microsoft 365 Copilot has its own semantic search engine across the Microsoft ecosystem. If you already have it active and it covers your needs, Summum IA's semantic search adds most value in repositories outside the Microsoft environment: documentation in legacy network drives, ERPs without a native Copilot connector, proprietary knowledge bases or historical files in other formats. For specific integrations with the Microsoft 365 stack, Summum Sistemas is the right track.
A standard project — one or two document sources, a basic search panel and no special security requirements — reaches production in four to six weeks. The first fortnight covers audit and index configuration; the second, team testing and relevance tuning; the final week, production launch and training. Highly heterogeneous repositories or on-premise deployment requirements add two to four additional weeks.