Marius Nigond and Antoine Nigond Elevate Travel Experiences with AI at iWander

Transcript

iWander, is your personal travel companion powered by AI.

Think about it almost as your own personal tour guide in your pocket, which helps you discover new destinations and new cities. We're a team that knows travel really well.

Too often you see people aimlessly walking back cultural sites or new cities without really discovering or learning anything behind them. Our mission is to provide those customers access to super high quality information and almost that same experience which you'd get as if you had a personal tour guide walking along with you every step of the journey.

One of the biggest challenges comes from the digitalization of travels. There's a growing dissatisfaction amongst operators who are finding it more and more difficult to compete for direct bookings.

So they're seeing their customer acquisition costs go up and customer lifetime value and brand loyalty go down. So we provide an engagement tool so that brands can keep engaging and connecting with customers throughout their entire trip.

So our business model is B2B2C. We've actually built our technology to be plug and play for travel brands and we have a number of different deployment solutions.

First, we have an SDK. It's our most popular solution where we can plug our technology directly into third party apps. Then we can deploy white label apps for our customers if they don't necessarily have apps themselves.

And finally, we have an API coming out later this year. When working with larger travel brands, it's imperative that we're enterprise ready and that our models are fully customizable and they can be hosted in their own environments.

And that's where DataStax comes in. Astra's vector capability is really one of the foundations of our products and lends itself to so many different aspects of the product. I think one really powerful example is the product recommendations.

So for example, we vectorize the whole product catalog and having this product catalog in vector format allows us to search on more complex queries.

For example, what we might do is not only search on a user query, but we can also factor in user interests. And this allows us to unearth some really kind of relevant products that might be ignored in a more traditional search against a relational database.

Another powerful example of vectorization is in vectorizing partner data and ingesting proprietary content from potential partners. This allows that content to be returned to the end user and also using Astra DB allows us to guarantee security for the partner.

Developers often spend so much time kind of cleaning and structuring data and I think one of the biggest benefits of Astra DB and certainly the vector capabilities, is the ability to search on more unstructured data.

The standout feature for us is really the ability to search in real time against the vector database. The team at DataStax do a great job maintaining and iterating these integrations continuously, so ensuring that the tech is always up to date and relevant. In the latest AI advancements,