Why Data Science Is Always Part of My Team

AI is the hottest trend no matter the industry. You can use it for anything you can imagine, it’s accessible to all. But there was a time (like two years ago) that this wasn’t the case. But before AI became the hottest trend and AI tools are everywhere, there was another way to add AI to your tool box: DIY, aka do it yourself, and in our case this meant hiring data scientists.

Unlocking insights through data science has been an integral part of how we build, operate, and scale our companies for the past years. In fact, we started working with AI back in 2019, long before the current wave of generative AI.

When we built JOGO.ai, we used computer vision and machine learning to capture, register, and analyse football players’ performance. This was a time when AI wasn’t in every startup pitch deck, but that has changed rapidly.

How OpenAI and Others Changed the Game

The AI landscape changed dramatically when OpenAI, Anthropic, Google DeepMind, and other companies started making AI models widely accessible. Suddenly, cutting-edge AI wasn’t just for big tech, it became a tool that anyone could incorporate into their work.

This shift reinforced what we already believed. AI isn’t just a feature. It’s a foundational advantage. Every startup today has the opportunity to embed AI into its core operations, automating tasks, improving decision-making, and unlocking new possibilities.

How We Use AI and Data Science

At ve2, AI isn’t an afterthought. It’s a key part of how we build and invest. We use AI in three major ways:

1. AI-Driven Products

We've always believed that AI can be used to build powerful, intelligent products. From early projects like JOGO.ai to the AI-driven startups in our current portfolio, Scoutmate, MediaMorph, we focus on leveraging AI to create smarter, more scalable solutions.

2. Internal AI Tooling

AI isn’t just for external products, we also use it to improve how we operate. At ve2, we built an AI tool that reads and evaluates pitch decks, helping us quickly assess whether a startup aligns with our investment strategy. This allows us to focus on the best opportunities without wasting time on manual filtering. We do this with many repeatable tasks, automating as much as we can.

3. Deep Analysis

One of the most powerful ways we use AI is in deep analysis. Data science allows us to uncover trends, correlations, and insights that would otherwise go unnoticed. AI’s ability to process vast amounts of data in real time gives us a major advantage in staying ahead of the curve.

Why We Build Our Own AI Instead of Relying on Vendors

With so many AI tools available, why do we choose to build our own? Simple: ownership, customisation, and cost-effectiveness.

• Owning our data. When you use third-party AI models, you don’t fully control your data. We want to ensure that our proprietary insights stay within our ecosystem.
• Full customisation. Off-the-shelf AI tools are great, but they’re built for the masses. We develop our own AI to tailor it exactly to our needs and seamlessly integrate it into our portfolio startups' products.
• Cost efficiency. Relying on external vendors can get expensive at scale. By building our own AI models, we can optimize costs while maintaining full control over performance.

The Future of AI in Startups

AI is no longer optional. It’s a competitive advantage. That’s why we started ve2.ai , an initiative to help startups integrate AI at their core. Founders who embrace AI from day one will build smarter, more scalable businesses, while those who ignore it will fall behind.

If you’re a founder thinking about how to integrate AI into your startup, let’s talk. The future belongs to those who don’t just use AI but build with it.