Xenith Search
Semantic search that returns the document answering the question, not the one with the keyword.
Xenith Search returns the documents that answer a question, not the ones that contain the keyword. We connect to your data sources, build a hybrid semantic index (dense vectors plus BM25), tune relevance against queries your users actually ran, and hand you an API your applications call on day one. Relevance is measured on a labelled query set before and after tuning.
Weaviate / Qdrant vector store, managed and optimised by Xenqube
Deployed as a scoped engagement for your stack - not a self-serve trial or live sandbox.
How it works
- Natural language queries so users ask questions, not keywords
- Multi-modal search across text and images from the same index
- Federated search: one query searches across multiple disconnected systems
- Personalised ranking that learns from click behaviour
- Analytics dashboard: what people search, what they click, what they don't find
Who this is for
What's included
Typical engagement deliverables
Scoped to your stack and compliance needs. We deploy the working system into your environment and hand over runbooks - not a sandbox login.
Your catalogue or document corpus indexed and searchable
Search API live and responding to natural language queries
Relevance tuned against your real content
Analytics dashboard showing search patterns from day one
Embed code or SDK ready for your front-end team