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The TWIML AI Podcast · 3 min · Oct 15, 2026
Sequen is prioritizing ranking models over representations
Zoe Weil says Sequen is prioritizing improvements to ranking itself over traditional image and text representations it expects to commoditize. That shows where it believes its differentiating research effort lies.
Zoe Weil is co-founder and CEO of Sequen.
Sam Charrington, Host · Host, The TWIML AI Podcast
About this segment
Zoe Weil says Sequen is prioritizing improvements to ranking itself over traditional image and text representations it expects to commoditize. That shows where it believes its differentiating research effort lies.
From “Optimizing AI Recommendations for Revenue with Sequen Founder Zoe Weil”, Oct 15, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
“we're very, very focused, I would say on the latter, rather than focusing on things like that we think are going to be very commoditized”
Takeaways
- The team is focusing on making its core ranker better.
- Zoe expects some image and text representations to commoditize.
- Evaluation harnesses help guide that investment choice.
In this episode
Full episode- 4:17Retail ranking should optimize purchases, not just relevance7:35
- 15:52Sequen personalizes a session without tracking user identity2:57
- 39:29Recursive updates compound ranking gains without rebuilding models4:45
- 32:12Ad and organic revenue can rise together3:51
- 27:11Recommendation ranking changes after a shopper commits2:22
- 44:16Large mesh models relate multiple event streams2:31
- 18:53Sequen chose Rust for low-latency feature serving2:49
48:46Sequen is prioritizing ranking models over representations2:51
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 48:51
like a big unlock being like capturing more information from the user. Capturing the events at the browser is certainly complex, but doable. But then you talked about like the infrastructure to manage those events at scale. It's a lot. And then transform all of those in real time. And then know what to transform it into. Yes. Which, by the way, we have dedicated models to do that. So you'll literally have streaming data that's going in not to one model, but it's going into like hundreds of models.
- 49:25
which all have to then output that data, reconstruct it, put it into the final ranker, and have it all be served to the user in sub-20, sub-50, sub-100 milliseconds. It's an enormous infrastructure and modeling undertaking. With that in mind, when you think about kind of the technical horizon or the innovation opportunities, like what are the big opportunities to go after that will help you kind of push your system and approach further?
- 49:59
There are several routes that we're looking at right now without revealing too much. I think, you know, one of the biggest debates we've had internally is how much do we invest in traditional representation learning of images or texts and the like versus actually continuing to innovate on the ranking models. themselves and the nuances there. And I was talking about how our eval harnesses and the library there is absolutely critical to our success. And so I would say right now, at this point in time, anyway, we're very, very focused, I would say on the latter, rather than focusing on things like that we think are going to be very commoditized, if not already, you know, in terms of having multimodal representations of like hero images on company websites or whatnot.
- 51:06
So I would say that is probably where we're most focused. on are on things that actually make the ranking, the core ranking model itself better rather than the auxiliary components that are, that involve more traditional representation learning. We're going to leave that to the big image or video or other frontier labs to do. And open source projects will continue to benefit from that research as it, as it comes out.