The Exchanges

Q And is it, is it dominated by a specific use case? Like, were there customers that you feel like really represent the pinecone use case well?

A Yeah, a hundred percent. Uh, first text is probably most of what we see. Uh, nowadays models are really good at images and so on, but, uh, text is still the predominant data type. Notion Q&A now runs on, on Pinecone, and they serve essentially question answering with AI, uh, to tens of thousands and probably hundreds of thousands of, of their own customers. Uh, Gong does the same thing with sales calls. Again, serves all of their use cases for all of their customers, and so on. So one of the most common patterns is companies that themselves become trailblazers and innovators with AI, and they themselves hold a lot of their own users' or customers' text, and they want to search over it or generate information on top of it. That ends up being an incredibly common pattern.

AI assessment note: “Notion Q&A now runs on, on Pinecone, and they serve essentially question answering”

Q You mentioned Canopy is, um, to help enable more people to build RAG products. Like, where do you, where do you see developers or your customers struggle to get embedding space to AI products generally successful? Or what were you, what were you trying to achieve with, with Canopy?

A Yeah, so, the vector databases and Pinecone specifically are very foundational model, are very foundational pieces of technology. We're, we're very deep in the stack, and to build a, You know, a proper full end-to-end solution, say like Notion Q&A, there's quite a lot that you have to build on top of it. You have to ingest documents and, and what's called chunk them. You have to figure out how to break them into like factoids and pieces of information. You have to embed everything with models. You have to ingest them into the vector database. You know, when you get a query, you have to figure out how to manipulate it and how to embed that. You have You have to search over it. You have to re-rank. You know, there's, there's a lot. There's a whole system you have to build around it, and a lot of people told us that this is actually quite complex, and they're right, right? We put out Canopy as really an example. It is an end-to-end kind of cookbook. If you just take this, it should work. You should probably, once it works, you should figure out how to make it better for your own application, right? Because, you know, medical Data is not JIRA tickets, you know, and JIRA tickets are not Slack messages, and you might be building a different product, but at least you have some end-to-end starting point that already does something and you can start improving on.

AI assessment note: “We put out Canopy as really an example. It is an end-to-end kind of cookbook.”

Q example, you're, you're an HR company and you don't want different people's salaries to leak across. An LLM because you're using it as like a chat bot to help you with context regarding your own personal data in an enterprise or things like that. Can you talk a bit more about how embeddings can provide personalization and in some cases potentially other features that may be attractive to, to enterprises?

A Yeah. So that, that's a very common and reasonable thing to be concerned about. Data leakage can happen in, in two main ways. A, if you use a service for your foundational model that, that frankly, uh, retrains their models with your data or records it, right? Or saves it in some way that is opaque to you, right? That is a huge problem, and I think a lot of people are, a lot of people are struggling with that. The second is, if you're building an application in-house, whatever it might be, and you fine tune your models on added data, that added data might end up popping where it shouldn't in answers to, you know, other people's questions or whatever. What people do with vector databases is actually incredibly simple, right? You don't fine tune your model on your own proprietary data, at which point you know for a fact it doesn't contain any proprietary data, because it's never seen any of it, ok? And then at retrieval time, or at, you know, whenever you, you apply the, uh, the chat or the agent, you retrieve the right information from the database, Give it as context to the model, but only do inference. You don't actually retrain, you don't save that interaction, at which point that data doesn't exist anywhere. It's like an ephemeral thing. And the added benefit to that is, by the way, that you can be GDPR compliant. You can actually delete data. So if, if, you know, so, you kn…

AI assessment note: “at retrieval time... you retrieve the right information from the database, Give it as context”

Q And, uh, maybe just going back to like the traditional database market, like why not in my Postgres or my long or whatever I'm using already?

A Again, I mean, we, we see this in the market. A lot of people tell me, hey, I already use tool X or database Y and why not? And frankly, oftentimes when it's some tiny workload or just learning how to use embeddings for the first time and so on, it might actually work okay. It's when people try to actually do something in production, they're trying to scale up, they're trying to actually push the envelope, or they're trying to launch a product. They need to have some unit economics attached to it that makes sense for the, for that product. That's where people run into huge problems, and so, uh, many of them just, you know, start with us to begin with. To be honest, a lot of them are enthusiasts, and they actually kind of enjoy learning how to use a new kind of database, and are you, you know, user experience is smooth enough, and, and, you know, so many tutorials and notebooks and examples that they actually find it exciting, but I guess some don't, and that's, that's, that's fine.

AI assessment note: “It's when people try to actually do something in production, they're trying to scale up”

Q Yeah. Makes sense. What's next for Pinecone? Or what are, what are some major things coming that you'd like to talk about?

A So, I mean, there's a ton. We are, we're an infrastructure company. Uh, and so we obsess about ease of use and security and, and stability and cost and scale and performance. Also as a, an engineer at heart, I'm, I'm very excited about those things. And all of that is coming again. Serverless is becoming faster, bigger, better. Uh, more secure, easier to use. And we're starting to really grapple with, ah, what very large companies and very, you know, kind of trailblazing tech companies are going through. I said that getting AI to be truly knowledgeable is still complex. I think we're starting to grapple with deeper issues that, that the entire information retrieval community has been dealing with for about 4050 years now. We're starting to see those, you know, come to the fore in, in, in RAG and in AI in general.

AI assessment note: “Serverless is becoming faster, bigger, better. Uh, more secure, easier to use.”

Q so I was wondering what applications you had in mind, given that there's so much excitement around Pinecone for the AI world. The prior AI world had a slightly different approach to a variety of these things, and I'm just curious, like, were you thinking of different types of embeddings back then? Were you thinking of other use cases? Like, what was the original thinking in terms of starting Pinecone?

A The tsunami wave of AI that we're Going through right now, uh, didn't hit yet, but, uh, in 2019, the earthquake had already happened. Deep learning models and so on have already been grappled with. Large language models and transformer models like BERT and others started being used by the more mainstream engineering cohorts. You could already kind of connect the dots and see that where this is going. In fact, before starting Pinecone, I myself had founder anxiety between are we already too late versus nobody knows what the hell this is and we're way too early. And it took me several months of like wild swings between those two things until I figured maybe the fact that I have those too early, too late mood swings maybe means it's exactly the right time.

AI assessment note: “In fact, before starting Pinecone, I myself had founder anxiety”