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Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

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Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#11

Post by tullie »

sidcool wrote: post #6

What are the underlying ML models? Open source or custom trained?

We have a library with about 100 algorithms which you can choose from or by default we automatically choose based on your objective.

Majority of them are open source models we've forked and improved. Just as an example, we integrated in gSASRec last week: https://github.com/asash/gSASRec-pytorch, and added a couple of improvements on scale and the ability use language and image features. We use LLMs for the encoding of unstructured data, and we host these our self, although OpenAI and Gemini are used for error message parsing and intelligent type inference, things not on the real-time path.

More info here: https://docs.shaped.ai/docs/overview/model-library

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#12

Post by tullie »

hajrice wrote: post #9

How does it compare to Algolia?

The short answer is: we're better at recommendations and personalization and lean towards more technical teams (e.g. even with data/ML experience). They're better at traditional search and, these days, lean towards less technical teams.

Longer answer is in our blog post about it: https://www.shaped.ai/blog/shaped-vs-algolia-recommend :)

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#14

Post by esafak »

candiddevmike wrote: post #3

This seems like a tough build vs buy sell. For a lot (most?) companies, the search/recommendation system isn't necessarily optimized for the customer's search. Instead, it's a way to maximize revenue via preferred placement or inject ads. This almost always leads to a gigantic if/else chain of bespoke business analyst driven decisions for the marketplace. How are you going to allow folks to influence the system? Or d…

A company doing that doesn't understand LTV.

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#15

Post by hajrice »

tullie wrote: post #12
hajrice wrote: post #9

How does it compare to Algolia?

The short answer is: we're better at recommendations and personalization and lean towards more technical teams (e.g. even with data/ML experience). They're better at traditional search and, these days, lean towards less technical teams. Longer answer is in our blog post about it: https://www.shaped.ai/blog/shaped-vs-algolia-recommend :)

Cool! Any live demos we can try?

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#16

Post by tullie »

astronautas wrote: post #13

How does this compare to Vespa? If the key difficulty in scaling search is infra as you say, Vespa is an interesting alternative.

Compared to Vespa, we're much easier to get setup on. A big part of this is that we have real-time and batch connectors to all leading CDPs and data warehouses. E.g. if you're on Amplitude it takes Being quicker to setup, also means it's quicker to build and experiment with new use-cases. So you can start with a feed ranking use-case the first week and then move to an email recommendation use-case the next week.

In terms of actual performance and results, we've never gone head-to-head in an A/B test so i'm not sure the specifics there honestly!

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#17

Post by tullie »

hajrice wrote: post #15
tullie wrote: post #12

Earlier quoted context omitted.

The short answer is: we're better at recommendations and personalization and lean towards more technical teams (e.g. even with data/ML experience). They're better at traditional search and, these days, lean towards less technical teams. Longer answer is in our blog post about it: https://www.shaped.ai/blog/shaped-vs-algolia-recommend :)

Cool! Any live demos we can try?

Yes play.shaped.ai! We just opened that up in a gateless way for this post. Let me know what you think. I should also mention that these demo models are on our cold-tier so that it doesn't break things, in production there's a big speed up.

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#18

Post by gk1 »

Congrats (from Pinecone) on the launch! The e-commerce and media recommendation space desperately needs an AI-based solution without the lead-filled baggage of legacy search or recommender systems.

> 100M+ Users I assume you mean 100M+ end-users have interacted with a site or product that uses your technology. The way it's phrased sounds like you're saying Shaped itself has 100M+ users which of course it doesn't. Consider replacing that with "100M+ interactions" or something.

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#19

Post by deepskyai »

Congrats Dan and Tullie - and the rest of the team. Great to see AUSTRALIA and particularly Melbourne (formerly known as the most liveable city in the world) represented. Is there anything different now compared to what you released ~18 months ago? Or just launching on HN now?

Re: Launch HN: Shaped (YC W22) – AI-Powered Recommendations and Search

#20

Post by tullie »

gk1 wrote: post #18

Congrats (from Pinecone) on the launch! The e-commerce and media recommendation space desperately needs an AI-based solution without the lead-filled baggage of legacy search or recommender systems. > 100M+ Users I assume you mean 100M+ end-users have interacted with a site or product that uses your technology. The way it's phrased sounds like you're saying Shaped itself has 100M+ users which of course it doesn't. Con…

Thank you! Would love to catch up sometime assuming you're in NYC with the rest of the Pinecone team!

Yes by 100M+ users we definitely mean end-users, wasn't intentional to mislead so thanks for flagging -- we'll update.

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