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Wonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It LastWonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It Last

Wonderskin: The Viral Lip Stain That Launched a $125 Million Empire — And How We Made It Last

Jun192026

Table of Contents

The blue goo looked like a science experiment gone wrong. Metallic, electric, unnatural. I watched as my husband Michael painted it across his lips, and I thought, “What have we done?”

It was 2020. The world was locked down. Salons were shuttered. And we were sitting in our London home, staring at a formula we had spent months developing in our own lab — a peel-off lip stain that went on blue and revealed a long-lasting tint underneath. It was strange. It was bold. It was, frankly, a little terrifying.

“We knew we had something different. But different doesn’t mean successful. Different just means… different.” 

We launched it quietly. No big PR push. No celebrity endorsements. Just a product, a website, and a hope.

Then, something happened. A creator posted a video of the reveal — that moment when the blue masque peeled away to reveal a perfect, long-lasting stain. The comments exploded. “Wait, what IS that?” “I need this.” “How does it work?”

Within weeks, that single product had racked up over one billion views across social media . By 2025, we had sold over 7.5 million units globally . One lip stain was selling every five seconds on TikTok Shop . And our annual revenue hit $125 million — up from virtually nothing just four years earlier .

But here’s what nobody tells you about going viral: It’s terrifying. Because once you have the world’s attention, you have about five seconds to prove you deserve to keep it.

Wonderskin didn’t become a beauty industry success by accident. This case study explores how a single viral lip stain helped build a $125 million brand, the marketing strategies behind its explosive growth, and the lessons entrepreneurs can apply to their own businesses.

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You’re Sitting on a Goldmine of Your Own Data: The Story of Glean and the Future of Enterprise SearchYou’re Sitting on a Goldmine of Your Own Data: The Story of Glean and the Future of Enterprise SearchYou’re Sitting on a Goldmine of Your Own Data: The Story of Glean and the Future of Enterprise SearchYou’re Sitting on a Goldmine of Your Own Data: The Story of Glean and the Future of Enterprise SearchYou’re Sitting on a Goldmine of Your Own Data: The Story of Glean and the Future of Enterprise SearchYou’re Sitting on a Goldmine of Your Own Data: The Story of Glean and the Future of Enterprise Search

You’re Sitting on a Goldmine of Your Own Data: The Story of Glean and the Future of Enterprise Search

May112026

It was 2019. I was at Rubrik, the data security company I co‑founded, trying to pull up an internal document from a specific project in our knowledge base. Fifteen minutes of Slack searching. Another ten in Google Drive. More time in Jira, Confluence, and the company wiki. Every tool promised to make work faster. Instead, I was burning an hour on something I knew existed.

That was the moment I looked at my screen and said out loud: “I built search systems at Google for years—why can’t I find my own company’s data?”

“Arvind Jain couldn’t find his own company’s data at Rubrik. So in 2019, before ChatGPT, before the AI boom, he built Glean, the first enterprise generative AI company.”

The irony stung. I had spent more than a decade at Google as a distinguished engineer leading teams across Search, Maps, and YouTube, and then helped build Rubrik into one of the fastest growing companies in cloud data management. Yet inside my own organization, finding information was harder than searching the entire public web. Every SaaS tool was its own silo—Slack, Google Workspace, Jira, Salesforce, Confluence, Figma, GitHub, ServiceNow. Companies had spent billions digitizing their operations, only to leave employees with disconnected fragments: a knowledge graph splintered across hundreds of apps.

I walked away from a comfortable role at a successful company—a “cozy Google job,” as some called it—to fix a problem no one realized was crippling the modern workforce. I was not chasing hype. ChatGPT did not exist yet. The phrase “generative AI” meant nothing to most people. But I understood what nobody else seemed to grasp: the most valuable AI wouldn’t train on public internet data. It would train on your company’s private data, understand your permissions, and speak your language. If you could connect the dots across an enterprise’s fractured knowledge graph, you could build something exponentially more valuable than general‑purpose search.

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Kai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without FundingKai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without FundingKai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without FundingKai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without FundingKai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without FundingKai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without FundingKai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without FundingKai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without Funding

Kai Collective Case Study: How Fisayo Longe Built a £2M Fashion Brand Without Funding

Apr282026

Kai Collective is a London‑based contemporary womenswear brand founded by Nigerian‑born Fisayo Longe in 2016. Longe bootstrapped the company with an £8,000 ($11,000) loan from her mother after dropping out of university and leaving an auditing role at KPMG. The brand struggled for its first four years, generating only 23 orders at launch despite Longe’s 40,000 social‑media followers — a painful lesson that influence alone does not equal sales.

This Kai Collective case study highlights how Fisayo Longe navigated challenges to build her brand.

Everything changed in early 2020, when Longe wore a sheer, earth‑toned mesh dress to a BAFTA party. The Gaia dress went viral on Instagram and TikTok, sold out immediately, and generated an estimated £200,000 in revenue. By 2024 Kai Collective’s annual revenue had reached £2 million, with lifetime sales totaling approximately £6 million. The brand has been featured in Vogue, Elle, Marie Claire, and Forbes, and Longe was named to the Forbes 30 Under 30 Europe (Art & Culture) list in 2021.

Kai Collective remains 100% founder‑owned, has never taken outside funding, and prioritises community co‑design, size‑inclusive silhouettes (US 0–20+), and prints that celebrate the female form and African heritage. The brand’s enduring lesson: authenticity and resilience outweigh follower counts.

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From Apartment Startup to $11B Legal AI Operating System: The Harvey StoryFrom Apartment Startup to $11B Legal AI Operating System: The Harvey StoryFrom Apartment Startup to $11B Legal AI Operating System: The Harvey StoryFrom Apartment Startup to $11B Legal AI Operating System: The Harvey Story

From Apartment Startup to $11B Legal AI Operating System: The Harvey Story

Apr102026

It was July 4, 2022. My co-founder Gabe and I were in our San Francisco apartment, still living on a mattress on the floor, when we dialed into a video call with the entire C-suite of OpenAI. We had cold-emailed them two weeks earlier with nothing but a rough demo and an audacious 86% accuracy claim on a Reddit legal advice test. They didn’t laugh us off the line. Instead, Sam Altman and his team agreed to meet on a national holiday. That moment felt like a strange, quiet inflection point—the legal industry was about to shift, and we had somehow secured a front-row seat to the future of the legal AI operating system reshaping the industry.

Two years later, that casual July Fourth call had evolved into a fundamental transformation of how legal work gets done. By early 2025, Harvey had surpassed $50 million in annual recurring revenue, expanded to 235 customers across 42 countries, and locked in the majority of the top 10 U.S. law firms as clients. Today, over 100,000 lawyers across more than 1,000 organizations globally run their most critical work through our platform. We didn’t just build another legal tech tool; we built the underlying operating system for how AI and law will coexist.

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