Cool idea. I've been working on an adjacent product (serendipityrecs.com), but mine is more targeted towards consumers as opposed to B2B. I think I gravitated towards B2C by default because as an engineer, I don't want to deal with sales, but your product makes sense. I'll be interested in following your progress over the next few years to see how your thesis plays out.

Couple questions

- How well do your recommendations hold up against Amazon's? Since you're scraping the metadata, you should be able to generate recs for Amazon items from their own catalogue. This might be an interesting product / demo for potential customers.

- Once you hook up your system to your customer's back end, how do you learn from the behavioral data you get from them? That's straightforward for cf/mf, but can be tricky to integrate into what you already have. - You talk about Stripe pooling the data from their customers. I think the analogue for you would be pooling the behavioral data from your customers as opposed to the metadata. Have you thought about this?

- It sounds like you're doing nearest neighbors on the vector representation. You may already know this, but LSH is a fast way to do this when you have many items.

- Do you embed all items from your different customers into the same vector space? That would be ideal from the POV of creating a pooled dataset that would be helpful for all future customers, but sounds tricky given that everyone likely has their own idiosyncratic system.

Best of luck! Lmk if you'd like to talk shop sometime, I also have several years of experience with recommender systems (my email is in my profile).