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The 11th Future Unicorns cohort brings together companies building across AI, infrastructure, sports, logistics, and media, all at a similar inflection point: moving from validated products toward global markets.
While their domains differ, a common thread runs through all of them. Each is tackling a problem that already exists at scale, but remains structurally unresolved at scale, whether in how businesses operate, how software is built, how goods are distributed, or how people consume content.
As they prepare to engage with the US market, the focus is often on validation: which customer segments adopt fastest, which positioning resonates, and what kinds of partnerships, investor conversations, or early pilots can confirm market fit.
Here’s a closer look at what each company is building, and what they are aiming to test next.
Rebuilding how digital businesses operate
Skillplate is building what it describes as a fully AI-powered business operating system, designed to reduce the operational friction that slows down early-stage execution.
The platform aims to replace fragmented tooling with a single system that handles onboarding, product setup, marketing, analytics, and even elements of business strategy. The underlying assumption is that most founders don’t fail due to lack of ideas, but due to slow and inefficient execution.
Their focus now is on identifying the right entry point in a highly segmented US market and validating what messaging actually converts.
“The global problem we’re solving is universal: entrepreneurship has too much operational friction. People don’t fail from lack of ideas or ambition; they fail because execution is slow, fragmented, and error-prone.”
Building sales infrastructure for fragmented global trade
nFuse is addressing the fragmented network of corner shops, kiosks, small retailers, distributors, and sales reps that still operate with almost no digital infrastructure.
Their approach is to deploy AI agents through messaging platforms like WhatsApp, automating ordering, recommendations, and account management for consumer goods companies.
Already live across Central and Eastern Europe, the company is now looking to translate that model into the US by understanding how to build relationships with distributors and commercial teams in a new market context.
“Over $4 trillion in consumer goods moves annually through 15 million fragmented trade outlets… We’re building nFuse to be the vertical sales infrastructure for this channel.”
Turning fragmented athlete data into actionable intelligence
Barin Sports is tackling what it calls a “fragmented intelligence” problem in elite sports, where data exists across multiple systems but fails to translate into coherent decision-making.
By integrating performance, health, and biometric data, the platform aims to predict non-contact injuries with 90% accuracy and increase high-intensity performance by 20%, a need the team says is already visible in Europe, where top leagues lose nearly €1 billion annually to non-contact injuries.
With the US representing a higher-stakes environment, the company is now focused on understanding organizational structures, regulatory constraints, and how to position its model within professional franchises.
“An athlete’s data is trapped in isolated silos across different departments. This ‘disconnected view’ leads to contradictory decisions, preventable injuries, and wasted millions in player value.”
Reducing manual work in logistics procurement
AVEO TECH is focused on a persistent inefficiency in logistics: the manual nature of quotation and procurement processes, despite the presence of SaaS tools.
The company estimates that standard workflows still require dozens of hours monthly across a large global market of freight forwarders and brokers.
Having validated its model, the team is now looking toward larger markets like the US, where legal aspects, accounting, hiring, go-to-market, and identifying the right matchmakers become the next questions to solve.
“The manual work is still there… about 40 hours per month on a very standard procedure.”
Automating one of the last manual layers of business communication
Vihra AI is building voice-based AI agents that handle customer conversations across sales, support, and operations, a channel that remains largely manual and difficult to scale.
Their system enables businesses to respond instantly, qualify leads, and manage interactions in multiple languages, addressing a bottleneck that exists across markets.
As they move toward the US, the focus is on understanding how quickly companies are shifting from experimentation to full integration of AI in core operations.
“Businesses miss key opportunities simply because they can’t answer every call or respond instantly.”
Rebuilding how fans discover and experience sports
Sportforia operates at the intersection of sports media and AI, focusing on how fans discover and engage with content in a landscape that has shifted toward short-form, mobile-first consumption.
The platform surfaces archival and underutilized content through AI-driven discovery, addressing both user behavior changes and monetization challenges faced by rights holders.
With fans already following sports across borders, from European football to the NBA to tennis year-round, the company is now exploring how to position itself within the complex US sports ecosystem.
“Fans want short-form, personalized, mobile-first sports content, but the experience is fragmented.”
Creating the infrastructure layer for AI-generated code
Bitloops is building infrastructure for a rapidly emerging problem: the lack of persistent context in AI-generated code.
As coding agents become widely adopted, the absence of structured memory and reasoning leads to degraded code quality, reintroduced bugs, and loss of architectural coherence.
The company is positioning itself within a category that is being actively defined, with a focus on establishing an open-source standard for how this layer operates.
“Every session starts from a degraded context. What lands in the repository is the output with none of the intelligence that produced it.”
Addressing “Comprehension Debt” in AI-driven development
CodeBoarding focuses on what it calls “Comprehension Debt,” a growing issue where developers can no longer fully understand the code produced by AI agents.
As generation speed increases, visibility decreases, making planning harder and eroding the codebase over time.
The company is targeting early adopters in the US who are already deploying AI agents in production environments, aiming to validate both market timing and initial design partnerships.
“Developers ship logic they no longer understand, which erodes the codebase and eventually makes AI prompts less effective and planning impossible.”
Across all eight companies, the pattern is consistent. Each team is working on a problem they describe as global in scope, and each is using the program to test how their product, positioning, and market strategy hold up beyond their home market.
The US is a different environment, one where distribution, expectations, and decision-making dynamics shift.
As the program kicked off, we’re here to support these teams as they pressure-test their ideas, navigate a new market, and build the foundations for what comes next. Looking forward to the weeks ahead!
