Contextual AI Search
Smart search turns raw product and content data into an intelligence layer that boosts discoverability, user satisfaction, and revenue — while slashing manual tagging and rule-based upkeep.
Vector search converts text, images, or structured product data into high-dimensional embeddings. Instead of matching keywords, it measures semantic similarity between a query and items in your database, surfacing results that share meaning — not just matching strings.
Key Advantages
- Understands natural-language queries & synonyms
- Semantic relevance—matches meaning, not just keywords
- Handles typos, slang and multi-language
- Combines text, images & metadata in one vector
- Scales to millions of items via fast ANN indexing
Who Should Use It
- E-commerce sites with large catalogues
- Media / knowledge portals needing smarter search
- SaaS apps delivering personalised recos
- Chatbots or voice assistants handling free-form questions
- Any org with unstructured data (docs, tickets, notes)
Impact on Business
- ↑ Conversion – fewer zero-result pages
- ↑ AOV – auto cross-sell on relevant queries
- ↓ Support cost – users self-serve
- Differentiated UX vs. keyword search
- Future-proof layer for recos & chatbots