Vector search · AI content chunking · Multilingual retrieval · Contextual discovery
Traditional keyword search returned mostly irrelevant results for nuanced teaching and content queries.
Users repeated searches and left sessions because useful content was hard to discover.
Users spent too long searching and scanning without finding the right passage.
Content relevance needed to work across languages and phrasing, not only exact terms.
Similarity retrieval finds meaning-aligned content instead of relying only on exact keyword matches.
Long transcripts and articles are broken into theme-based searchable segments.
Search results are ranked and summarized so users can quickly understand relevance.
The retrieval layer supports broader language and phrasing coverage for a global audience.
Match accuracy for relevant content discovery.
Relevant results delivered without long browsing sessions.
Broader access across languages and phrasing styles.
Contextual results replaced poor keyword-only matching.