How Georgios Built an AI-Powered Analytics Platform for E-commerce

AI is everywhere today. But that wasn’t the case in 2017 when Georgios Grigoriadis launched Baresquare. This innovative analytics company benefited from Georgios’s vision before AI was mainstream. In this interview, he shares behind-the-scenes details about starting and growing the business.

Tell us about yourself and your business.

I’m Georgios, founder and CEO of Baresquare. I founded the company out of a passion for developing a product that could make a real difference. Coming from an MBA background in Belgium, I moved on to data analytics at Sony and discovered my love for the field.

In 2014, I had a career-altering realization. Data analytics was infested with systemic inefficiencies. Marketers were overwhelmed, staring at dashboards all day and not getting results from their data. I felt there had to be a better way.

Fast-forward to 2017 and Baresquare SaaS was born. We set out to create an AI-powered platform that would solve these issues and untie marketers’ hands to amplify their impact. We built it from the ground up, using generative natural language before LLMs.

Today, we specialize in eCommerce, enabling marketers to make a direct impact on their revenue. I think we’ve completely changed the analytics model because we deliver actionable insights in plain English to the right people at the right time. But further than that, we’ve moved on to developing our AI agents to execute the necessary actions as well. I feel that this completely revolutionizes the way businesses take advantage of their data.

How does your business make money?

We’re in the process of shifting from volume-based pricing to performance-based. This way, our fees are aligned with the actual impact on revenue, savings, or margins we deliver. When our clients win, we win. I think it’s the honest, viable solution.

What was your inspiration for starting the business?

Starting out in 2017, the connection between data and intelligent software hadn’t yet become so apparent. I saw an untapped potential, I thought data could really drive transformative results. At the time, we didn’t have all the answers, and neither did anyone else, but we knew we were on to something big.

We experimented extensively, testing various approaches to really get the most out of data for exponential outcomes. Today, we’re trying to crack the code and make data truly work for our clients—accurately, reliably, and at scale.

In 2024, we call this Agentic AI.

How is the business funded? 

For most of our early years, we bootstrapped, as one does, especially coming from a no-name town in tech. We invested around $2M of our own funds to develop the initial SaaS platform and eventually caught the attention of strategic investors who shared our vision for healthy, long-term growth and who could appreciate our commitment to building a robust, scalable solution.

They injected an additional $3M into the business, which has been instrumental in accelerating development and market reach.

How did you find your first few clients or customers?

Conventional business wisdom tells us that a company has to choose between edge innovation and customer intimacy or leading with cost efficiencies, which has never been our focus. We’ve always believed that the future—way smarter software solutions that can prescribe action—belongs to those who dare to balance both.

In the early days, we made a conscious decision to reject the either/or mentality. We knew that to truly make our mark in AI, we had to push beyond what everyone else was doing in digital analytics while also cultivating deep relationships with our clients to really understand their needs. We became a part of their worlds, lived with their challenges, and listened to their aspirations. At the same time, we constantly tweaked cutting-edge AI solutions that could transform their businesses. 

Looking back, it’s clear that our commitment to balancing edge innovation and customer intimacy was key to attracting our first few clients – and to building a company that would ride the storm of AI. If it’s not broke, don’t fix it, so we’re sticking to this philosophy as we pioneer the next generation of AI solutions.

At the speed that tech is advancing, we believe the companies that will win are those that can innovate while staying grounded in their customers’ real needs. And that’s precisely what we’re built to do.

What was your first year in business like?

I guess, like any entrepreneur, taking a road trip without a map, a compass, or a destination, you feel hopeful but lost. I was optimistic about the unclear market and really confident about the work I knew my team could pull off, but I often found myself wondering, and I, too, was often lost.

The UK proved to be a welcoming starting point, offering a business-friendly environment for a SaaS startup like us. With the legal groundwork laid and available off-the-shelf, I was free to focus on defining our unique value proposition in what was starting to look like a really crowded data analytics market.

You build from scratch, you fail, you learn as you go along, and you pivot, nothing new here, which is easy to say in hindsight. But all and all, that critical first year was some of the most productive and formative of our entire journey. And the most emotionally satisfying.

What strategies did you use to grow the business?

We experimented with various strategies to grow our business in the early days, including cold calls, networking events, content marketing, and a freemium launch on Product Hunt. However, we quickly realized that some of these approaches were not as effective as we had hoped.

Cold calls or any sort of outreach, really, proved to be an unproductive strategy. We tried different flavors, but none stuck as a strategy that brings win-win value. We found that most people were not receptive to unsolicited calls, and it felt like we were intruding on their valuable time. We don’t like it when it happens to us, so we stopped.

While helpful for building connections, networking events often attracted experienced buyers who were looking for vendors in specific, well-established product categories. As an innovative startup offering a new category of solutions, we struggled to find the right fit and generate meaningful leads from these events.

Our freemium launch on Product Hunt provided valuable insights into our product-market fit. We discovered that our expertise was built in a way that didn’t align perfectly with the expectations of a typical freemium model, which often requires a product that’s easy to install and use independently. This experience taught us that many companies needed extra support and guidance to effectively transform their data into actionable insights, a realization that prompted us to refine our approach and focus on a more hands-on, time-to-first-value customer experience.

As we kept muddling our way through, we found that expanding use cases with existing clients and leveraging their success stories for word-of-mouth referrals proved to be the most effective growth strategy for our edge technology. We might go back to some of the tried-out strategies as we grow.

What was the biggest challenge you had to overcome?

Waiting for the market to catch up to your vision is like lighting a fire with damp wood. It’s damn hard, and only time can save you. That’s the challenge we faced at Baresquare as we built our anomaly detection triggers & action workflows in natural language for years, long before the GenAI technologies landed in our hands. The biggest challenge we faced in adoption was the human bottleneck in understanding, hypothesizing, and acting on the insights.

Our growth story has some parallels with a startup called Pinecone. They started working on vector databases in 2019, well before the rise of large language models (LLMs). It was hard for them at first, they were arguably too early and struggled to gain mass adoption. However, by investing in the right things for years, they found themselves in the perfect position when the market turned. Now, they’re riding the wave.

At Baresquare, we’ve been building the foundation for AI agents for a long time. Our triggers and action flows were in place, but they lacked the ambiguity-handling capabilities of LLMs. Now, post-LLMs, we look to be at the same inflection point as Pinecone. We’ve built a great company and are now poised to get really lucky with the shifting market.

But waiting for a spark is a hell of a tester. It feels like you’re stuck in slow motion while the world around you moves at breakneck speed. Keeping your team motivated and focused during these times is a constant challenge.

Looking back, it taught us the power of perseverance and agility, and these lessons have become by now part of our DNA.

What have been the most significant keys to your business’ success?

Solving real problems. While many startups do interesting things, they don’t always address genuine issues. Our experience in the industry helped us identify a universal problem, leading us to create a solution that resonates across sectors. Focusing on actual needs has been the key factor of our success.

Tell us about your team.

Our team is truly one-of-a-kind. I feel lucky because they’re in it for the long haul. It’s not just my dream,, and that’s very rare. We all share a belief in revolutionizing the way we work with data. We have extensive expertise in every function, but what sets us apart is a deep understanding of data-driven decision-making, from data quality to specialized workflows and automated actions.

What is the most important lesson you’ve learned growing the business?

Leading a startup requires taking responsibility for the team’s direction and outcomes. My upbringing instilled this sense of ownership in me. I think the most important lesson I’ve had to learn is embracing the paradox of control.

Objectively, nothing is really in our control, including our upbringing. Yet, to maintain clarity and purpose, both individually and as a team, it’s essential to act as though we have agency. Without this belief, tools like OKRs lose their meaning and effectiveness.

Balancing these two conflicting narratives had bogged me down for years—accepting the journey’s inherent unpredictability while still taking purposeful action and assuming responsibility. 

What separates your business from your competitors?

Baresquare stands out as a GenAI-native platform, leveraging proprietary anomaly detection and natural language generation algorithms to provide rapid access to insights and actions. Our deep expertise and pedigree in building enterprise-grade solutions set us apart from both established players with rigid, pre-LLM data architectures and newer startups offering a little more than just thin ChatGPT wrappers.

What is your favorite quote?

“Do. Or do not. There is no try.” Although Yoda in Star Wars lacks hands-on startup experience, I love this quote because it reminds me to focus on outcomes, not just efforts. Our COO, Lars Boeddener, has his own favorite quote coming from a Japanese colleague that has more or less the same meaning: “There is a difference between doing the dishes and having clean dishes.”

What are some of your favorite books, blogs, podcasts, or YouTube channels?

I stay informed with The Neuron, an AI-focused daily newsletter, and occasionally listen to the Freakonomics podcast. “Death by Meeting” has been a game-changer for our team’s productivity, while “The Hard Thing About Hard Things” has been a real reference point for the loneliness and the struggle of building a startup through moments of crisis. In my path, these were capital controls in Greece, a David vs Goliath RFP that would put us out of business, Brexit, and everything we all know very well from the pandemic to date.