AI Recommendations
Turn every answer into the right recommendation.
Every image tap, mood board upload, and structured answer feeds an algorithm that ranks your catalog in real time — category by category, with a reason for every pick.
The reality
Filter grids don't understand "rustic but not country."
Your customers know exactly what they want. They just can't type it into a search bar. "Luxury but not stuffy." "Coastal, but works for the office." "Modern, but warm." They know the feeling. They don't know the SKU. So they scroll, get overwhelmed, and leave — or they call a rep who has to start from zero.
What changes with Suggestly
Every answer quietly builds a profile. By the final screen, the algorithm has enough signal.
As the customer moves through the guided flow, their inputs build a style and requirement profile in the background. Budget. Venue type. Cover count. Visual tone from images they tap or photos they upload. By the time the last screen lands, the AI doesn't guess — it ranks. A curated shortlist, category by category, with a reason for every pick.
How answers become recommendations
Declared preferences from structured inputs
Budget, project scope, use case, occasion — structured questions lock in the hard requirements first. These act as the outer boundary before the algorithm ranks products by fit within that boundary.
Visual style signals from images and photos
Image selection grids and mood board uploads extract signals the customer could never put into words — tone, texture, era, formality level. The algorithm reads what they respond to visually, not just what they say they want.
Product relationships mapped at catalog enrichment
This plate pairs with these bowls. This range complements that glassware. Compatibility relationships are mapped when your catalog connects — so the shortlist is coherent, not just individually similar. Customers get a set that works together, not a pile of loosely related items.
What changes when recommendations are earned, not guessed
- Ranked shortlist, not infinite scroll — customers see the best options first, not the most options
- Reasoning shown per pick — customers understand why, not just what
- Swap or refine in real time — every rejection sharpens the next suggestion
- Learns from every session — accepts and rejects improve future recommendations
Features that deliver this
Let your catalog answer the question the customer actually asked.
See the recommendation engine running on your products in a live walkthrough.