Semantic search ranks information using learned representations of meaning. It can help when a question and a relevant document use different wording. It remains a retrieval technique: a result that is semantically similar may still be irrelevant, outdated or inaccessible to the user.
A useful deployment starts with a search problem and evidence about the current workflow, rather than an assumed percentage of employee time that a new tool will recover.
Why keyword and semantic search behave differently
Keyword search is valuable for exact identifiers, names and phrases. Semantic retrieval can find related concepts expressed with different words. Neither method consistently wins for every type of document or query.
Consider a hypothetical search for how to request time away from work. A document titled “Annual leave procedure” may be relevant even if it does not use the exact phrase in the question. For an invoice number or technical error code, exact matching may be more useful.
The main technical steps
Prepare the collection
Read approved sources, extract text and preserve metadata such as source links, dates, owners and permissions. Scanned documents may need OCR. Duplicate and conflicting versions can affect the results even when the retrieval model is working correctly.
Create embeddings
An embedding model converts text into numerical vectors. A search system can compare a query vector with document or passage vectors. The similarity measure and embedding model influence ranking; they do not determine factual correctness.
Retrieve and rerank
Initial retrieval produces candidates. A reranker can compare candidates with the question in more detail. Filters may restrict results to a particular date, project or user permission level. Test the whole pipeline, including filters, rather than evaluating only the embedding model.
Present evidence
Show enough context for the user to judge the result and open the original source. Distinguish retrieved passages from generated summaries. If a language model writes an answer from the results, the system also needs RAG evaluation and controls for unsupported claims.
Hybrid search
Hybrid search combines keyword and semantic signals. It can be useful where users ask conceptual questions but also search for exact codes, names or contract references.
The combination adds tuning decisions: how candidates are merged, how results are ranked and whether reranking improves the intended tasks. Compare it with simpler baselines on the same labelled questions.
Potential applications
The following examples are possible uses, not measured client case studies.
- Procedures and documentation: find a relevant policy even when the question uses different terminology.
- Support records: retrieve similar incidents for an agent to inspect.
- Research: locate related publications or technical notes for further review.
- Contracts: retrieve candidate passages while leaving interpretation and final conclusions to the responsible reviewer.
- Recruitment information: locate relevant material only within a clearly defined, lawful and reviewed use; similarity alone should not determine a hiring decision.
A search query requiring exact numerical conditions or a complete list may need structured extraction and validation. Semantic similarity alone does not reliably answer questions such as which contracts exceed a particular liability threshold.
Working with Polish and other languages
Evaluate representative documents, abbreviations, inflected terms and domain-specific expressions. A model’s general multilingual support does not establish its quality on the organisation’s collection.
Compare candidate models and retrieval methods using the same evaluation data. Fine-tuning may be considered when there is suitable training data and evidence of a problem it can address. It is not automatically required for Polish or guaranteed to improve every query.
Measure retrieval quality correctly
Create a set of questions with labelled relevant results. Include easy cases, ambiguous queries, exact identifiers, missing information and restricted documents.
- Precision@k: the proportion of the top k results that are relevant.
- Recall@k: the proportion of known relevant results found within the top k.
- Mean Reciprocal Rank (MRR): the average reciprocal position of the first relevant result across queries. It ranges from 0 to 1; higher is better, and a query with no relevant result in the evaluated list contributes 0.
- Task success: whether users can find and verify the information they need.
- Latency and cost: how the system behaves under the expected workload.
Agree acceptance criteria for the specific task. A single aggregate score can hide failures on sensitive queries, language groups or uncommon document types.
Security and operation
Apply access controls before exposing retrieved passages or passing them to a language model. Check whether derived indexes retain information after source permissions change or records are deleted.
Map the data flow through extraction, embeddings, storage, reranking, model services, logs and backups. Local deployment is possible for some components, but a self-hosted application can still call external services. Verify the configured implementation and applicable processing arrangements.
Assign owners for source updates, relevance evaluation, access reviews, monitoring and recovery. Search quality can change when documents, terminology or models change.
Budget and rollout
Budget for data preparation, integrations, indexing, evaluation, interfaces, infrastructure and ongoing maintenance. The appropriate architecture depends on the data, query volume, latency requirements and operational skills.
A practical sequence is to define the task, inspect sources, test a limited collection, evaluate results and then plan wider use. Dates and costs should follow that scope. A successful demonstration does not establish readiness for every source or user group.
BeHive’s role
BeHive is QA10’s open-source research engine under the MIT licence. It supports source-based research, claim assessment and exploration of entity relationships. You can install it with pip install behive.
A company-wide semantic search service also needs a defined collection, source integrations, access controls and operating arrangements. Those requirements should be assessed separately rather than assumed to be included as ready-made enterprise features.
FAQ
Does semantic search replace keyword search?
Not necessarily. Exact matching is useful for identifiers and precise phrases, while semantic retrieval can help with different wording. Compare keyword, semantic and hybrid approaches on the actual queries.
How many documents are needed for it to be useful?
There is no universal threshold. The difficulty of the questions, document structure and current search behaviour matter more than the count alone. A small evaluation can show whether semantic retrieval adds useful value.
Must documents be sent to a cloud service?
That depends on the selected components and configuration. Some models and indexes can run locally; other services process data remotely. Map and verify the complete data flow before using sensitive documents.
How long does deployment take?
The schedule depends on source access, preparation, integrations, permissions and quality criteria. Agree a pilot scope and use its findings to plan production work rather than assume a fixed duration.
What is the difference between semantic search and RAG?
Semantic search retrieves information using semantic similarity. RAG adds a language model that prepares an answer using retrieved material. RAG therefore needs evaluation of both retrieval and generated claims.
Discuss your search problem
Contact QA10 with example questions, the relevant sources and who needs access. This gives us a concrete basis for discussing the next step.