search-knowledge

Semantic search retrieves brand knowledge by interpreting a natural-language query and returning ranked insights with provenance. It applies multi-layer search across vector similarity, knowledge graph relationships, and local unsynced indexes to find the most relevant stored learnings.

Yes, you can filter brand knowledge search results using precision filtering. The search supports filtering by content type, date range, tags, priority, and optionally includes expired knowledge to ensure you retrieve the exact insights needed.

Multi-layer recall queries connected memory services including a vector database, a knowledge graph, and a cross-session store, plus a local unsynced index. It then merges, deduplicates, and provides transparent result coverage reporting for accurate brand insights.

You can find competitor insights and voice guidelines by querying stored brand learnings. The search interprets your natural-language question, applies filter-aware ranking across campaigns, and surfaces relevant marketing strategy or compliance information with provenance.

Yes, semantic search identifies causal and temporal relationships across campaigns. It applies multi-layer search across vector similarity and knowledge graph relationships to answer performance questions about what worked in past marketing efforts.

The best way to deduplicate insights is through multi-layer recall, which queries vector databases, knowledge graphs, and local indexes simultaneously. It merges results, applies filter-aware ranking, and removes duplicates while reporting transparent result coverage.