Name: The road from AI prototype to product
Uploaded: 2026-10-08T15:45:45.931Z
Duration: 14 min 48 s
Description: The road from AI prototype to product

Transcript for "The road from AI prototype to product": Everyone. Welcome. My name is David Abramson. I'm the CTO here at Curve A, and I'm pleased to be sharing some thoughts with you on AI prototypes to product. I'm gonna jump right in and share my screen, and we can get started talking about, this very sort of interesting and prescient topic. I think one of the things that we're seeing is how AI tools can be an amazing prototype accelerator. But where we're gonna think and focus on is how, we can get those AI built features, to close the gap between what makes a successful prototype and getting your product production ready. Just real quick, I wanted to give a little bit of background about Curvy. We are, a a specifically built embedded analytics tool for SaaS companies who want to provide a rich set of analytic capabilities to their tenants and users. And this is a 100% of what we do, so all of our customers are SaaS companies, SaaS product teams like yourselves. Who are looking to provide rich set of of embedded analytic capabilities to their customers, tenants, and users. Now, AI coding tools have absolutely fundamentally changed how product teams build. I'm sure many of you have seen, working features come together in just an afternoon or maybe even a couple of hours. So the the speed is definitely real. And the question is, what do we do with it? But this is this is kind of the story that's generally the the really good news. For sort of idea and feature validation, AI tools, can be some of the best options available. Low cost, and the ability to show stakeholder something real instead of, you know, just specifications or mock ups, as well. Now, the proof of concept, however, is is really designed to answer the one fundamental question, could the feature that I'm building work? A product has to survive the prototype, and the jump between sort of these two boxes here, is really where a lot of teams and product development can stall. And this is not just me or Kirby speaking here. I've actually been posting and talking a lot about this, not only in my, you know, LinkedIn, but also to other SaaS leaders about this topic for the last several months. And everyone's sort of saying the same things, figuring out how to close that gap between kind of the AI accelerated prototype and getting into a full production ready solutions. So I think that there's a trap here that we don't wanna fall into, and that is, sort of that AI built prototype, trying to convince us that we're already there. And, you know, I think there are a lot of things that we wanna make sure that we're not overlooking when it comes to, you know, how do we secure the solution, how do we scale it, how do we support it, and all the, the customer facing requirements to get to production ready, which is incredibly important when launching new features. Now we're in the analytics space, customer facing analytics space, so this is actually a perfect example for using AI to get to sort of that proof of concept prototype stage because, I think one of the easiest things to do is to start creating these outcomes. Like, a chart can just appear. The sample data that you're working with looks correct, and it sort of feels like it's ready. But, you know, every concept really needs then to sort of, solve for the main challenges that those AI prototype tools aren't really gonna get you, into that sort of production mode. So I think what we need to think about is what are those challenges? What are the key challenges that we want to not ignore when we're going from sort of that prototype phase, whether it's with customer facing analytics or any of the other tools that we're working with or any of the other feature sets that we're working with within our products, that we wanna get into production, as well. So, let's talk about, some of the challenges that we see and that we've heard from other SaaS leaders that sort of present themselves when you're starting from that AI powered prototype and trying to get into production. Probably the first major one, that we're hearing often is all about handling security. Security is generally the first thing that the prototype skips out on and and the last thing that you can really afford to get wrong. Right? Especially in something like a customer... Customer facing analytics solution, concepts like record level security, tenant level security have to be and must be enforced, for everything. So every report, every dashboard, every drill down, every chart that you build has to enforce that security logic. And making sure that when you do actually go into production, concepts like that multi tenant security, is is fully there, is fully baked, and is not overlooked from that sort of AI generated code or the prototype that you started with. Challenge two that we also hear quite often, from the folks that we're talking to, is all about how do we get it to scale. I think one of the things that is appealing about the sort of AI generated code, especially for a proof of concept or prototype, is that, it's obviously gonna be, written and using sort of the data that was made available to it. So the queries that you're running, they look great when it's working off of a sample file or a small subset of the data just to get that proof of concept, in front of the stakeholders or in front of, customers who need to validate it or, you know, these types of things. But actually going up against your production data systems and production tail... Tables and all of the different tenant environments, tenant workspaces, is... Can present a whole different challenge. So scaling performance, scaling all of the... To getting all the data that you want, that's where kind of that real engineering effort is going to happen and where, again, oftentimes, the AI generated prototypes are going to sort of fall down, in that process. So, performance at scale is gonna be incredibly important. And, this one certainly goes very much hand in hand with the previous one, which is all about the security. Again, oftentimes when you build something from a prototype stand... Standpoint, you're not necessarily thinking about, all of the tenants, maybe just looking at a single tenant use case and then the data that goes with that. And so thinking about how am I going to secure it and then how am I going to scale it, is is kind of the the key challenges that you wanna make sure you're paying attention to as you're looking to scale from that AI powered prototype into the production mode. So all of those challenges are certainly things that you wanna make sure, you're looking at and paying attention to. And then probably the the third challenge, and this might be sort of, the least obvious challenge among, the ones we're identifying today, and that is the long term sort of maintenance, customization and ownership of the solution. And we see this a lot when it comes to, embedded analytics use cases, and that is once you start putting these features in front of your customers, in... Inevitably, they're going to ask for more. So it's all about, hey. Now that I see this report or this dashboard or these other features, around my data, how do I, add to that? How do I get that next chart... That next dashboard? With tools like Curvy, we tend to solve that with capabilities like full self-service, where you can actually empower the customer, to create, their own content and create their own outcomes as part of that process. But, again, you wanna make sure that it's not just about getting kind of the first iteration in front of the stakeholders, in front of the customers. It's all about how do you then continuously maintain and grow the solution to match to sort of all of the things that the customers are gonna continue to to ask for. And then, you know, one of the things that we've also seen kind of in this, embedded analytics space is this can become sort of like a full time job. Right? Responding to the customer requests, maintaining that solution, building out a full featured, you know, roadmap set of, capabilities for your customers so that you have a steady pipeline of new enhancements and new capabilities that are are coming down the way. And, you know, again, leveraging AI to potentially prototype these new features or leverage a combination of tools to prototype these teachers, these features along with self-service capabilities baked into products. All of that can come together to create kind of a a full production system, that you have that, allows you to, present to your customers exactly what they need, and and maintain the system long term, so that you're... They're getting what they need, overall in the in the long run. Now I think, essentially, those challenges then become sort of the three questions that we wanna ask ourselves, before we ship any of these features maybe that we're we're starting with AI. So instead of just saying, hey. I built this in a couple of hours. It looks great. Let's go ship it. Those three challenges. Can I handle the security? Can I handle the scaling? Can I handle the ongoing maintenance support and management of those features? Instead of just saying, hey, let's get it out the door and and ship it. Let's, assess whether or not we can, handle all of these challenges, and essentially, save us, a huge amount of headache in cleanup if we don't actually address these items upfront. So, really, the point of kind of all this framework is to understand, how we make this a correct decision. So, really, the worst outcome would be ship something and it's not complete, it doesn't secure, it doesn't scale, and it's hard to maintain. And in fact, if we decide, hey. Let's just throw this away, that's actually a success because the prototype validated our idea. Either it killed it quickly because it wasn't the right, feature set or outcome, or, it was a a great outcome, customers loved it, or stakeholders loved it, and now we can actually put that into production. So I think, again, all of these are are just questions that you wanna ask yourselves as you're going forward with your, your products and your features and you wanna be able to leverage, AI to get there. Now, I think sort of to recap everything here. So what do we wanna take away from this? You know, number one, I'm a huge fan and a huge proponent of you... Of of using AI to sort of aggressively validate and prototype the concepts. I think, obviously, as I said earlier, a lot of us have already seen these proof of concepts get generated maybe even in a a day or an afternoon. It's a great way to learn. It's a great way to validate ideas. It's a great way to, get stuff in front of stakeholders extremely quickly. But, those three costs are gonna matter. The security, how does it scale, and how do we maintain? Those are gonna be really important, and you wanna make sure that you're addressing those questions before we actually do, get into production. And then you wanna make sure that, again, you're you're handling all of that so that the demo or the POC or the prototype doesn't become this huge amount of sort of tech debt that you're having to deal with, in the long run. Now the last thing I just wanna, sort of leave you with is at Curvy, we've really architected our solution to help with this process. We have a platform that allows you to create the production ready systems but also incorporate AI models and AI tools, including MCP servers and AI agents that you can even customize to start creating those, proof of concepts, prototypes, and even just getting started, with delivery of, the, you know, the capabilities, the customer facing analytic capabilities that you wanna provide, to your to your customers, to your end user, to your tenants within the solution. So I'd love for, you know, to chat more with with all of you about this. You can certainly connect with me on LinkedIn. I've actually been posting about this topic a lot, and this is where we've we've actually heard a lot of the same feedback from, colleagues and other SaaS leaders where they're saying, hey. Yeah. We're we're doing a lot with AI to prototype, but we're also trying to take into account all of these challenges and considerations. So, shoot me a note, send me an email, visit us at curve a dot com. You can, see a demo of the software, but, yeah, I'd love to to chat with everybody and hopefully continue discussing this wonderful topic of of how we work with AI effectively and how we go from that POC or prototype into production. Thank you so much, and, hope to see you on the next one.