Amid AI and tech startups, Eastseabrother proved the power of demand and trust.
Updated
January 23, 2026 10:41 AM
Cats having a jolly good time with a can of tuna. PHOTO: UNSPLASH
At a Silicon Valley pitch event crowded with AI, SaaS and deep-tech startups, the company that stood out was not selling software or algorithms. It was selling pet treats.
Eastseabrother, a premium pet food brand from South Korea, ranked first at a Plug and Play–hosted investor pitch competition in Sunnyvale. The product itself is simple: single-ingredient pet treats made from wild-caught seafood sourced from Korea’s East Sea. The company follows a principle it calls “Only What the Sea Allows”, working directly with regional fishermen while avoiding overfishing. With no additives and minimal processing, what sets Eastseabrother apart is not novelty, but control—over sourcing, supply chains and consistency.
That clarity helped the company walk away with both Best Product and Best Potential. “Investors asked detailed questions about repeat purchase rates and customer feedback, not just our technology or supply chain”, said Eunyul Kim, CEO of Eastseabrother. “That told us the market is shifting—real consumer trust now carries as much weight as a compelling tech narrative”.
What truly caught investors’ attention was not an ambitious vision of the future, but concrete evidence of traction today. Eastseabrother has already secured shelf space in specialty pet stores across California, New York and North Carolina, including an exclusive partnership with EarthWise Pet, a national specialty retail chain. At a consumer showcase at San Francisco’s Ferry Building, the brand recorded the highest on-site sales among all participating companies.
At its core, the pitch was built on simplicity: one ingredient, clear sourcing and a defined customer need. In a market saturated with complex products and abstract claims, that focus and transparency stood out.
The judges’ decision also reflects a broader shift in venture capital thinking. Not every successful startup is built on complex software or high-tech innovation. In categories like pet care—where trust, quality and transparency shape buying behavior—execution and credibility can matter more than technical sophistication.
Today, Eastseabrother has extended its reach beyond the U.S., expanding into Singapore and Hong Kong, with additional plans to grow further in North America as demand for premium pet food rises. And the broader takeaway from this pitch is not that consumer brands are overtaking tech startups. It is that investors are increasingly focused on fundamentals: who is buying, why they are returning and whether the business can sustain itself beyond the pitch deck.
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A look at how motivation, not metrics, is becoming the real frontier in fitness tech
Updated
February 7, 2026 2:18 PM
A group of people running together. PHOTO: FREEPIK
Most running apps focus on measurement. Distance, pace, heart rate, badges. They record activity well, but struggle to help users maintain consistency over time. As a result, many people track diligently at first, then gradually disengage.
That drop-off has pushed developers to rethink what fitness technology is actually for. Instead of just documenting activity, some platforms are now trying to influence behaviour itself. Paceful, an AI-powered running platform developed by SportsTech startup xCREW, is part of that shift — not by adding more metrics, but by focusing on how people stay consistent. The platform is built on a simple behavioural insight: most people don’t stop exercising because they don’t care about health. They stop because routines are fragile. Miss a few days and the habit collapses. Technology that focuses only on performance metrics doesn’t solve that. Systems that reinforce consistency, belonging and feedback loops might.
Instead of treating running as a solo, data-driven task, Paceful is built around two ideas: behavioural incentives and social alignment. The system turns real-world running activity into tangible rewards and it uses AI to connect runners to people, clubs and challenges that fit how and where they actually run.
At the technical level, Paceful connects with existing fitness ecosystems. Users can import workout data from platforms like Apple Health and Strava rather than starting from scratch. Once inside the system, AI models analyse pace, frequency, location and participation patterns. That data is used to recommend running partners, clubs and group challenges that match each runner’s habits and context.
What makes this approach different is not the tracking itself, but what the platform does with the data it collects. Running distance and consistency become inputs for a reward system that offers physical-world incentives, such as gear, race entries or gift cards. The idea is to link effort to something concrete, rather than abstract. The company also built the system around community logic rather than individual competition. Even solo runners are placed into challenge formats designed to simulate the motivation of a group. In practice, that means users feel part of a shared structure even when running alone.
During a six-month beta phase in the US, xCREW tested Paceful with more than 4,000 running clubs and around 50,000 runners. According to the company, users increased their running frequency significantly and weekly retention remained unusually high for a fitness platform. One beta tester summed it up this way: “Strava just logs records, but Paceful rewards you for every run, which is a completely different motivation”.
The company has raised seed funding and plans to expand the platform beyond running, walking, trekking, cycling and swimming. Instead of asking how accurately technology can measure the body, platforms like Paceful are asking a different question: how technology might influence everyday behaviour. Not by adding more data, but by shaping the conditions around effort, feedback and social connection.
As AI becomes more common in consumer products, its real impact may depend less on how advanced the models are and more on what they are applied to. In this case, the focus isn’t speed or performance — it’s consistency. And whether systems like this can meaningfully support it over time.