Unlocking Innovation in AI Ad Tech and Data

(00:00:00)

Welcome, everyone, to another edition of AI Insiders. Today, we’re excited to have three fantastic presenters. I’ve especially been looking forward to this one since I’ve known Jessica Pahoulis and Michael Miror from Hannah Grey VC for a long time. Jessica is one of the firm’s founders, and it’s always great to meet VCs who come from the agency side. This background gives them a unique lens on spotting talent and teams and knowing how to apply it, especially when connecting tech with marketers. 

We’ll see about eight-minute overviews with demos from each presenter, followed by Q&A, so please be ready with questions. We’re taking a bit more structured approach than usual; presenters will speak, then we’ll have a designated Q&A. As always, we want this to be interactive, so feel free to share any questions or thoughts in the chat. Jess, would you like to introduce yourself and the team?

(00:49:00) 

Absolutely. Thank you, David, and everyone for having us—we’re thrilled to be here. A bit of background on Hannah Grey: we’re a $51 million venture capital firm based in New York, Denver, and LA, investing primarily in very early-stage companies, often pre-product and pre-revenue, that focus on creating genuine connections with customers and spotting new opportunities.

I spent 10 years in agencies, starting at Zenith Media, Initiative, and then Evolution, one of the first innovation-focused boutiques connecting brands with startups. This is where I met David, and we hit it off right away as we both have a passion for helping startups find their first customers and guiding big brands on how to commercialize with emerging tech. After Evolution, I managed Stagwell’s corporate VC arm, investing in early-stage Martech and Adtech. A few years ago, my partner Kate and I launched Hannah Grey and recently closed a $51 million fund.

Today, you’ll meet some of our portfolio founders who are at the forefront of marketing technology, data analytics, and consumer brand transformation. These founders love questions, feedback, and anything that challenges their thinking. We really appreciate the opportunity for them to hear from you, so please engage openly. 

(02:31:00)

We’re kicking things off with Glisten, and I’ll introduce Ethan, the founder, based in San Francisco. Ethan, I’ll hand it over to you.

(04:01:00) 

Thanks, Jessica. We’re thrilled for this opportunity and excited to share what we’ve been working on. To set the stage, brands are increasingly aiming to engage with a broader range of influencers. Previously, brands focused on top-tier creators, but now there’s a move towards micro- and medium-tier influencers who often have highly engaged, niche audiences. While this shift opens up new possibilities, it also presents a challenge: scaling these efforts while understanding each creator’s unique audience.

The tools that currently exist don’t provide enough insight. They typically focus on basic metrics like engagement rates, which don’t reveal why a creator resonates with their audience. Two creators may have the same engagement rate for completely different reasons. With AI and advancements in natural language processing, we can now turn subjective data into measurable insights. Our focus at Glisten is to understand the “why” behind each influencer’s connection with their audience, making authentic alignment between brands and creators possible.

Our platform, which originally helped creators grow authentic communities, now includes a feature for brands and agencies to track aggregated trends across communities. I’ll walk you through a real-world example using Glisten’s dashboard.

(09:04:00) 

We’re looking at 110 Instagram creators and 63 YouTube creators for a celebrity-driven cosmetic launch targeting acne-related products. Glisten’s main features include lists and lenses. A list is simply a selection of creators, while lenses are tools that help you analyze that list. Using AI-powered semantic searching, we match creators with campaigns based on content relevance rather than traditional keyword frequency metrics.

For instance, when we search terms like “brown skin,” Glisten reveals top creators who resonate with related topics. Brands can analyze posts to see why a creator aligns with their lens, gaining insights into tonality, emotional quality, and brand safety, among other attributes. The platform updates in near real-time, so brands can monitor evolving campaign impacts and influencer dynamics. 

(16:51:00)  

Thank you for the questions—happy to stay connected for anyone interested in a deeper dive. We’ll be sharing an Airtable form for follow-ups.

(22:37:00)  

Our next presenter is Josh Bowen from Big Co. I’ve had the privilege of supporting Josh’s earlier company, CrowdTwist, which was acquired by Oracle. Now, Josh and his team are back, focusing on transforming SMS and customer experiences for brands. Josh, over to you.

(23:15:00)  

Thanks, Jessica, and great to see some familiar faces. Quick story before we dive into the demo—everyone get your phones ready, as this is participatory! My previous company, CrowdTwist, started as loyalty software and was later acquired by Oracle. Working with brands, we realized that while SMS is an incredibly direct channel, it’s mostly used for basic promotions and lacks real interactivity.

With Big Co, we’re changing that by using AI to deliver rich, personalized experiences over SMS. We use Amazon Bedrock’s AI models, which allows us to pick from various models like Claude, and we run retrieval-augmented generative AI to generate responses based on brand FAQs and other content. So, let's dive into a quick demo.

(29:37:00)  

Let’s kick off the demo. Scan the QR code on screen to join in. What we’re doing here is using AI to turn a transactional SMS into an interactive experience. Type “skin” to see the old method of collecting data, then type “demo” to see how we now use AI to capture structured data from a conversation. This allows for real-time profile updates and segmentation in a way that no one else is doing.

(36:57:00)  

We’re seeing significant traction in skincare, beverages, and retail. Big Co’s approach makes SMS conversational and interactive, addressing a major pain point for brands: traditional SMS campaigns lack engagement and personalization.

(40:32:00) 

Thank you, Josh! And now, we have Chaz Flexman, founder and CEO of Stard. Chaz is applying data science to identify pent-up consumer demand, helping develop products that are data-driven and highly relevant for today’s market. Over to you, Chaz.

(41:01:00)  

Thank you! Our approach at Stard is to build data-backed products that address genuine consumer needs. Think of what Shein does in apparel—where they create items based on consumer demand trends. We aim to do the same in food and beverage. Our data platform analyzes consumer-generated content, public sentiment, and retail trends to identify and predict where the next big consumer demand lies.

We’ve built a custom Consumer Data Platform that tracks trends across platforms like TikTok and Instagram, analyzing about 10 million posts per week. Using generative AI, we segment consumer needs and translate those insights into product briefs for our R&D team. For instance, we recently identified a rising interest in high-protein, non-soy-based products, which led us to develop a chickpea-based topper now performing well in retail.

(48:27:00)  

Thank you, Chaz. We’re seeing that data-driven insights are essential in refining product packaging and ensuring products meet evolving consumer demands. For those interested in learning more, I’ll add a form in the chat to connect with our presenters. David, back to you.