One intelligent layer for personalized discovery.
Explore RankNexa’s approach to connecting user behavior, catalog attributes, and context through recommendation and ranking infrastructure.
Book a demoSame catalog. A different experience.
A lighter workspace
Everyday carry
Explore the interactive illustration. Sample catalog and behavior are illustrative.
A useful next step, wherever discovery happens.
Personalized recommendations
An approach that combines behavior, content, and context for suggestions tailored to an individual or session.
Retrieval & similar items
Bring content similarity, semantic representations, and interaction signals into candidate selection.
Ranking with controls
Order candidates around relevance objectives while respecting eligibility, availability, diversity, and category constraints.
Session context
Consider recent clicks, views, saves, and other appropriate events alongside historical preferences.
API delivery
Connect recommendation retrieval, event tracking, catalog ingestion, and re-ranking to an application workflow.
Evaluation & experimentation
Assess strategies through offline comparisons, controlled experiments, and product-specific guardrails.
Keep your results.
Reconsider their order.
Re-ranking works on a candidate list from an existing search, feed, or retrieval system. The aim is to reflect the user’s context while keeping business constraints explicit.
RankNexa’s focus is recommendation and relevance infrastructure. Discuss your existing retrieval system, candidate metadata, and evaluation goals to assess fit.
Discuss your ranking workflowGreat catalogs.
Even better discovery.
Bring your product, your catalog, and your biggest relevance question. Let’s talk about what a better experience could look like.
Book a demo