Semantic search
We can search for related content using vector representations of text, while considering filters such as category or date.
Search by meaning with Qdrant.
We use vector search to find related content, explore company knowledge and build recommendation applications.
Tell us what you needWhere it fits.
We assess potential uses for Qdrant against your work and existing systems.
Company knowledge and AI
We design applications that retrieve relevant passages from documents for a language model. Source freshness and user access permissions are part of the application design.
Recommendations and matching
Similarity between products, content or other items can support discovery and matching. Results are evaluated against real examples.
How we can help.
We define the scope around your needs, existing systems and team priorities.
Data and model choices
We assess content preparation, chunking and vector generation with a suitable model. We plan filter fields and how source changes reach the index.
Explore AI-assisted development with AI LABSIndex and search design
We configure collections, indexes and filters for the use case. On suitable versions, we assess hybrid and multi-stage search and compare results using sample queries.
Explore training and mentoring with HGM Labs (opens in a new tab)Integration and evaluation
We connect search to your application and address access rules, tenant separation, deletion flows and operational needs together.
Explore software development with Reformo (opens in a new tab)Example scenarios.
These examples illustrate what could be built for different needs.
Explore 16 use cases
- User question
- Embedding model
- Qdrant search
- Relevant passages
Documents are indexed in advance; the application handles authorization.
Search company documents
An employee can find relevant passages in documents they are permitted to access. The application can show the retrieved sources in an answer flow.
Discover similar products
A store can suggest related products using their descriptions. Conditions such as category and stock can narrow the results.
More use cases.
These scenarios illustrate potential applications. We assess the requirements of the chosen version and deployment together at the start of a project.
Company knowledge and support4 examples
These examples need a suitable embedding model and an application that enforces the source access rules.
- Internal guidance. An employee finds relevant passages in current guidance they may access; the application shows the sources alongside the answer.
- Similar support tickets. A support team can review earlier tickets similar to a new one, filtered by topic and organization.
- Product documentation. A user can find help pages for a problem described in different words and see the relevant product version.
- Exploring long documents. A team can narrow relevant report passages by date and source type; people remain responsible for the interpretation.
Similarity and discovery4 examples
Similarity depends on the chosen embedding model; image examples need a compatible image model.
- Related products. Similar products are retrieved from descriptions; availability and stock are checked separately against current source data.
- Content suggestions. Articles similar to one being read can be suggested using language and topic filters; relevance is evaluated with examples.
- Visual product search. An image embedding can be compared with catalogue image embeddings to retrieve visually similar candidates.
- Possible duplicate content. A new item is compared with similar records; a merge or deletion decision follows verification.
Hybrid and multi-stage retrieval4 examples
Hybrid and multi-stage queries require the Query API available in Qdrant 1.10.0 or later. Suitable models are selected for any dense or sparse vectors used.
- Terms and meaning together. A help centre can combine and rank candidates found by keyword-oriented and semantic retrieval.
- Codes and descriptions. Exact fields such as product codes can be filtered while vector search finds products with similar descriptions.
- Reranking candidates. Passages found in an initial search can be assessed in a second stage with a suitable reranking model.
- Relevance experiments. A team can compare retrieval and fusion settings using sample questions and review the resulting rankings.
Data and access workflows4 examples
A tenant or user filter alone is not authorization; access must also be enforced by the application and infrastructure.
- Tenant-scoped search. An application serving several organizations can be designed to return results only from content the caller is authorized to access.
- Updated documents. A change pipeline can reprocess the affected chunks and vectors when a source document changes.
- Deleted records. An application-level deletion flow can remove indexed content when it is deleted at the source.
- Monitoring search quality. A team can periodically review relevance and data freshness using sample queries and feedback.
Product documentation: Qdrant hybrid query documentation (opens in a new tab) · Qdrant multitenancy documentation (opens in a new tab)