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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

#21

Post by tullie »

deepskyai wrote: post #19

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?

Australia represent! Although we're based in NYC we still are a mostly Aus/international team over here, it's great!

The biggest change is some of the less sexy stuff, like scale and security. E.g. we're now able to scale to 100M+ MAU companies with 100M+ items, and we have a completely tenant isolated architecture, with security as a top priority.

We've also made the platform more configurable and lower levels and we've found that people like choosing their own models and experimenting rather than just relying on our system.

Finally, we launched search only a couple of months ago and are currently heavily focused on building a best-in-class experience there.

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

#22

Post by jvans »

How do you personalize to the specific signals of the product, do they ingest into your infrastructure? What happens if a customer discovers a bug in a feature they're ingesting, how do they have control of retrains/pinning model versions? Who handles monitoring, the customer or your service?

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

#23

Post by tullie »

jvans wrote: post #22

How do you personalize to the specific signals of the product, do they ingest into your infrastructure? What happens if a customer discovers a bug in a feature they're ingesting, how do they have control of retrains/pinning model versions? Who handles monitoring, the customer or your service?

Yes when integrating Shaped you connect up the data sources needed to ingest: interactions, items and users. The Shaped interface then allows you to select which exact fields should be used for creating a Shaped model. We provide a full SQL interface to do this, which gives a lot of flexibility.

Our dashboard provides monitoring to help understand what data is ingested and view data quality over time. We expect customers to monitor this but also have alerts on our side and jump in to help customers if we see anything unexpected.

The dashboard also shows training metrics over time (how well does the model predict the test set after each retrain?) and online attribution metrics (how well does the model optimize the chosen objective?).

Customers can disable retraining if they want (which is essentially pinning the model version to current), we can do model version rollbacks on our side if we see an issue or if requested but it's not a self-serve feature yet. Because we've made it easy to create or fork a Shaped model, we've seen customers often create several models as fall-backs that rely on more static data sources or are checkpoints of a good state.

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

#25

Post by rwb_1912 »

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…

Dan here. On bespoke business decisions—these are handled through SQL and model configs. This involves deciding what items to filter, how to set the objective function (what to optimize the model for), and controlling exploration and diversity in the results.

Setting the objective function is often the most challenging. Different teams may prioritize different objectives and often it requires balancing multiple at once! For instance, how does a company think about the types of user engagement and long-term metrics like retention? A model optimized for clicks might be worse for retention in some cases, but not in others. Ultimately, we A/B test to find out. Surprises and counter-intuitive results are common!

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

#26

Post by bobek »

AnujNayyar wrote: post #7

Congratulations on the launch. We've weighed up algolia, in house, type-sense etc and so I'd would have been very keen to know more, but asking for us to integrate before knowing the pricing is a tough sell. Would highly recommend having at least an estimated pricing calculator so we can determine if its worth our time to install.

Definitely check out what https://recombee.com is doing (API doc is at https://docs.recombee.com/ ).

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

#27

Post by astronautas »

tullie wrote: post #16
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 t…

Thanks, so it's connectors, nice differentiators. Seamless integrations are harder than it seems.

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

#28

Post by password4321 »

tullie wrote: post #10
AnujNayyar wrote: post #7

Congratulations on the launch. We've weighed up algolia, in house, type-sense etc and so I'd would have been very keen to know more, but asking for us to integrate before knowing the pricing is a tough sell. Would highly recommend having at least an estimated pricing calculator so we can determine if its worth our time to install.

Thank you! Would love to chat, we've had several customers come over from Algolia and they've seen significant uplift. I can share more if you want to message me at tullie@shaped.ai. Our pricing is competitive with Algolia's to give you an idea there. We really wanted to get pricing calculator done get before this post but ran out of time. Keep an eye out over the next month for it to come up!

> pricing is competitive with Algolia's

Not sure if good or bad based on HN complaints about Algolia's pricing

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