Unusual Academy Pitch Deck

Unusual Academy Pitch Deck

We're forever grateful to the Unusual Academy folks for the support they gave us throughout the quarter. That being said, we thought we'd post our deck here to reflect our learnings on the business side of things, and how we ultimately told our story.

Intro

• A bit of a dumb question, but: we’ve probably all gone shopping in-person before.
• You know: you go to the store, show them your current fit or sample inspo, and you talk to a rep about which shirt goes well with which jeans.
• Now, contrast that with shopping online.
• I’ve personally had some pretty bad experiences with online shopping:
• I can’t tell you the number of times I’ve tried to find cool posters for my dorm room, got a bunch of poor search results, and ended up leaving the site without buying anything.

Problem

Problem Statement

• These types of experiences are the reason that search on e-commerce websites is so critical.
• Almost half of all users go immediately to the search box when they go to an e-commerce site
• And customers who search are more likely to buy items than not, driving more overall revenue.
• Users have also expressed increasing frustration when overall shopping experiences feel unpersonalized.

Competitors

• There are numerous incumbents in the search space, including Algolia, Bloomreach, and Constructor
• However, despite the importance of search and personalization, these names are not perfect.
• Most are still built primarily on top of older algorithms like keyword search, which don’t capture user intent and provide generic search results.
• The sum of all of these components? An unpersonalized online shopping experience, which leads to a loss of conversion.

Solution

• Now, the current moment has seen a lot of hype around AI technologies that are able to better understand humans more than ever before.
• In any case, shouldn’t there be a shift in the way that we search and discover products online?
• We think there will be, and we want to be at the forefront. I’m Chris Pondoc, and I’m one of the co-founders of Kenja. We’re building a service that helps e-commerce companies integrate personalized AI search into their applications.
• Through just a couple of steps, we can turn arbitrary product catalog data into search APIs that can handle expressive text and image inputs, offer highly personalized results, and even explain the recommendations users see rather than locking them completely behind a blackbox.

Market

• The global e-commerce market is worth over 50 billion and is growing at a rate of 14% each year. Overall, there are 26.5 million e-commerce websites, which serve over 265 million digital buyers worldwide.
• Within the overall market, our ideal customer profile are mid-market e-commerce businesses: companies at about 500-2000 employees with anywhere from M to B ARR.
• Importantly, these businesses typically have a product catalog with around 4k-5k stock-keeping units, and are building using large e-commerce platforms, such as Shopify, BigCommerce, and Magento.
• We believe that while these teams are desperate for a more personalized search solution – as well as more eager to adopt AI, in general – they don’t have the engineering capacity or time to build better search in-house.
• As we continue to flesh out our product offering, we believe that penetrating a large part of this market will give us the right to sell to the enterprise.
• Finally, while we’re currently in the customer discovery phase, we have had some early conversations with executives at much larger companies such as Nike and Walmart and the insights have been promising.
  • Namely, most of our chats have indicated that search traffic as a whole has gone down on actual e-commerce websites in favor of other AI search platforms like ChatGPT and Perplexity.
  • This suggests a sense of urgency in building a more personalized shopping experience directly integrated into these company sites.

What's Next?

• Now, why are we right the team to build? Simple: our team of D1 rowers – and myself – has worked together for a long time, and have proven ourselves to be experts in the AI space.
  • Individually, we’ve published award-winning research papers in AI at Stanford retweeted by the likes of Andrej Karpathy and have built ML production systems at Amazon and Splunk.
  • Collectively, we’ve been working together on AI projects over the last 3 years, publishing papers in ICML workshops and open-source journals.
  • Overall, our team has expertise that covers the entirety of the modern AI stack, from building scalable architectures to delivering a world-class product experience.
• As for the capital ask: we’re looking to a raise pre-seed round to help us continue to build the prototype and start selling to our ICP.
  • We’re currently in the process of recruiting mid-market design partners, and we’ve also built an MVP of our algorithm and platform that’s received positive reception amongst Stanford student shoppers and potential customers.
  • We’ve also done all of this while mainly orchestrating APIs and using our own personal GPUs.
  • As we move forward, we want to use the money to continue to flesh out our product, buy compute for our own custom infrastructure, and of course sell and onboard customers.
• Lastly, we think that our north star extends beyond search.
  • When looking towards the future, we always think back to our team’s own experiences, especially reflecting on how shopping in-person is a natural and personalized experience.
  • While our plan is to start with search, we want to help transform every e-commerce website into an end-to-end personalized shopping experience, one built for both finding the products you’re looking for as well as discovering ones you would’ve never thought about.
  • If you’re interested in our mission to build the future of online shopping, we’d love to chat, and I also left our contact info up on the screen. Thanks again everyone!