“How to Structure Voiceflow Flows for Large Image-Based Catalogs
So I've got a challenge in front of me...
I’m building a conversational assistant for a client that sells 200+ appliances (TVs, air conditioners, fridges, microwaves, stoves). Instead of using a structured database, we’ll be manually uploading the product posters into Voiceflow using card/carousel blocks (each poster already has price, model, and size).
My approach so far:
Create category-based flows (e.g., TVs, ACs, etc.).
When the user asks for a category (e.g., “Show me TVs”), I route them into a card/carousel block that displays the right set of posters.
Add clarifying questions (for TVs: “What size?”) to route to the correct subset of images.
If the request is unclear or filters don’t match, I plan to fall back to showing all posters for that category so the conversation never stalls.
Allow topic switching mid-conversation (e.g., “Actually, show me microwaves”) without breaking the flow.
My question 🙏:
👉 What’s the best practice in Voiceflow for managing a large number of manually uploaded card images? (I saw something about OCR + tagging layer outside of Voiceflow and expose it via a custom API)
Is there a cleaner way to structure and scale this so it doesn’t become unmanageable with 200+ images?
Any tips from the team (or @Braden (Voiceflow CEO) ) on keeping this architecture scalable? I feel like uploading the images manually is not going with an "AI" approach.
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