Transcript
A (0:00)
Foreign.
B (0:08)
Let'S give a big Omni Talk welcome back to AWS's Danele Estropa Daniele is here to hand out this month's Retail Tech Startup of the Month award. Danielle, for those that may not remember, is the worldwide technical lead for AWS Partners in Retail at Amazon Web Services where he drives a technical strategy for AWS Partners in Digital Commerce, customer engagement and generative AI. Danelli, we're three months in to handing out this award and our first two Retail Startups of the month have been blowing up on YouTube, both with over 30,000 views so far. So let's not make the audience wait any longer, let's not keep them in suspense. Who is August Retail Tech Startup of the Month please?
A (0:47)
This month peak is Vodi and they are one of our partners. We've been working with them for for bit over a year now and they are pioneering making E commerce data ready for AI and AI agents by building these intelligent data infrastructure solution. And the reason I picked them this month is because everything starts with data, right? And we know that good data in means good data out and good results, good conversions for our customers.
B (1:25)
Danelli, what was it about this company that really made you pause and be like this has to be part of our ecosystem. What kind of set them apart from others that you're seeing in the space?
A (1:36)
The way we look at it is customer needs, right? And when looking at some of the customers challenges is product discoverability is one of those challenges. I'm sure we've all have been in that situation where we're trying to look for something to something and we cannot quite find the right thing that we have in our head, right? Whether because we're using different type of keywords or different search terms and many companies are tackling these by building a chatbot or investing in search technologies. However, what Vodi is doing is really tackling this issue at the root. So at the data, right, we know that bad data means bad results. So by tackling that we know that we can improve on many of these results. And what they are building is quite a sophisticated system that takes a messy product catalog in different shapes and forms and transforms that into an AI optimized data using state of the art multimodal large language model that they are specifically fine tuned for retail. And they achieve these in two ways. The first one is that data extraction structuring of data from different sources, whether it's PDFs, spreadsheets and different sources and they're extracting that data to build these solid data Foundation. On top of that, then they are injecting real world context to make this data optimized and available to AI agents and what will come in the future. Right. So their data will understand different trends like Balletcore, for example, or what makes a good Father's Day gift, for example. Right, right. And I think what's quite unique or what's quite impressive about what they're doing is the kind of results that they are, that they're already driving and that some of their customers are seeing. Their customers are seeing 10% improvements in conversion rates, which is huge. Right.
