Product Recommendations
Turn support conversations into revenue with relevant product guidance.
Customers ask product questions in chat because they need help deciding. The agent should answer clearly and then guide them to the best product, alternative, or add-on.
Catalog-aware
Cross-sell ready
In-chat product cards
Recommendation inputs
Use more than titles and thumbnails.
Good recommendations come from rich catalog context, not shallow keyword matching.
Product titles and descriptions
Surface the right products when customers describe a need in plain language.
Categories and attributes
Use size, use case, material, style, and other structured inputs.
FAQs and buying guidance
Bring helpful support and fit guidance into the recommendation logic.
Business rules
Respect pinned products, alternatives, or cross-sell logic that supports margin and availability.
Sales support
Handle uncertain buyers the way a strong sales associate would.
Answer the customer question, narrow the choice, and help them move forward with confidence.
- Recommend primary matches for the stated need.
- Offer alternatives when the first match is too big, too expensive, or out of stock.
- Suggest complementary products when the use case clearly supports it.
Recommendation examples
Show recommendation quality through real product questions.
That is more convincing than any high-level feature language.
Customer
I already have the daypack. What rain cover fits it?
AI.Support Agent
The StormShield 25L cover is the best fit for that pack. If you also want dry storage inside the bag, I can show a compatible packing-cube set.
Useful accessory guidance instead of a generic upsell.
Customer
I want something lighter than this trail shoe.
AI.Support Agent
The Ridge Lite has a lighter sole and a lower profile than the current model. If you still need extra grip for wet terrain, the Terra Flex is the better alternative.
Helps customers compare with confidence.