Short answer: Ivan Lee calls it AI native services: it feels like white glove consulting to the client, while internally a model factory picks the model, fine tunes it and handles harness engineering, privacy first.
Watch this answer from Episode 56 (starts at 14:57) · Episode 56 notes and transcript
From Episode 56: He Built Siri at Apple. Now His Private AI Runs Inside the FBI, Google, and Netflix., starting at 14:57.
Sam Nadler [14:56]: I have one last question, if you don't mind, before we jump into the news. And maybe it's obvious, but what is your typical client engagement look like? A client approaches you. They said, we're we're interested in the solution. Is it almost like a consulting agreement where you help them get set up? You help them choose the model that's most appropriate? You help them, set up the deploy it appropriately across their organization, ensure it goes well, and then is it kind of hands off after that, or just kinda walk me through what it typically looks like?
Ivan Lee [15:30]: Yeah. Great question. So there's a new term to describe what we do here in Silicon Valley. It's AI native services. And so to the client, it feels like white glove consulting. Right? They will describe the workflow that they want or the workflow that they currently operate. And, yeah, we'll do everything from recommend the model. We will talk through how complicated this is because sometimes it's as simple as deploy this, connect this to our internal Dropbox or OneDrive, and suddenly it works. Sometimes it's a more complicated agent. So there's a little bit of education in there on, like, hey. How much, work is this going to take? How long is it going to take? How powerful does the model need to be? But once we have all of that, internally on the Datasaur side, we have this proprietary model factory, essentially. We have this thing that automatically takes the kind of requirements and suggests which model is going to be best. We take the kind of workflow and the benchmarks and the historic data, and we throw it all in there, and it's... And it kinda fine tunes and does all the harness engineering and everything else you need to make that model actually work. Right? So to the client, it feels like consulting services, really speedy services.
Ivan Lee [16:41]: From our side, it's actually this kind of internal productized platform that builds all of this from a privacy perspective first.
Jordan Metzner [16:51]: That's awesome. Yeah. Awesome episode. Great interview. To your point of that time frame, it wasn't even... This... None of this was possible previously, and so that means, like, this is new... A new frontier and a new ability for, ChatGPT is awesome, so everyone's throwing their data out. And I think Sam said at beginning of episode, now you can actually, like, host your own models and get high level performance. And it seems like that's just the beginning of where, large companies will take this. Ryz Labs builds production grade AI for the world's most ambitious companies. Support engineers. Data scientists. AI architects. All working together. On your problem. While others talk about AI. Ryz Labs is already implementing it. Okay. Cool. Shall we jump into the news?
