Bounce Cofounders On Using AI To Overcome Challenges

Forbes

Bounce cofounders discuss how AI helps their company and some challenges.  Want to truly know your customer? Check out Bounce Insights. The Irish startup claims its software can determine exactly where your customers are, what they’re looking to buy, and even what packaging draws their attention. How? AI-powered software, of course. Founded by Charlie Butler, Rónán Dowling-Cullen, Joshua Stafford, and Brandon Dooley, Bounce Insights is a market research firm that gathers consumer habits to help brands like Coca-Cola, Unilever, and Tesco better develop and promote products

Transcript

00:00What have been technically some of the challenges, but also the opportunities

00:03that you're seeing, especially how much is changing with AI so quickly? What are

00:07you seeing from a technical side of things? Yeah, so definitely the advent of

00:10large language models and that really accelerated our progress. Like we've been

00:14AI first since the start, we built our own AI models and our own AI algorithms

00:19to run and automate our technology, but all of a sudden you now have LLMs

00:23behind the scenes that are kind of like the engine or the platform that you're

00:26building on top of, and the rate of innovation of those is incredible. So

00:30we're layering that into our survey design, into our analysis, but we get that

00:34automatic improvement because we build what we call LLM ops, where basically we

00:39are able to, whenever a new model comes out, we test it against all the other

00:42models based on the data we're using and whichever one performs best, we hotspot

00:46in. So we get this constant automatic innovation without having to invest in

00:49ourselves. So that's been incredible and really accelerated us kind of maybe two

00:53years ago. I think that's like the way I think about it is that the problem has

00:56changed in what we're trying to solve, just the tools we are using to deliver

01:00the solution has changed quite a lot. So we were trying to do a lot of this

01:02ourselves. It was really difficult because we just, we were trying to like, okay,

01:06how do we do survey design using technology instead of people? Like that was a

01:10highly complex problem to try and figure out. And then suddenly you could like

01:13fine tune a model to try and replicate how do you design a survey? What does a

01:16good survey look like and how can you retrieve that similar on the analysis? So

01:20it's accelerated kind of our journey towards our desired outcome. And that's been

01:24just, as I said, sometimes you're better off being lucky and good than good. Timing

01:28matters a lot with companies. And when the AI kind of wave and then when things

01:33started to improve, we found ourselves with a foundational set of customers, you

01:37know, we were profitable, making quite a lot of money for our size and we're able to

01:41like invest quickly, be agile with our clients so that they could test and co-create

01:46stuff with us and try and solve actual problems with AI. Whereas there's a lot of

01:50kind of, as you say, hype and what does it actually do for me? And yeah, it's kind of a better

01:54search. You know, that's kind of what people materialize it as. Whereas for us, it was

01:58we were able to go very practically with solutions to everyday problems and they

02:02just want us to use it. Like they still want the same problem solved. However, whatever

02:06technology we use to solve it, they don't really mind. And I think that's kind of an

02:10important thing for me on the commercial side because people are a bit scared of

02:12it going, oh, well, if, you know, I open the door, are you going to run through and

02:16then suddenly I don't have a job? Whereas it's actually just a means to solving the problem.

02:20Right.

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