Founded by Kamila Zahradnickova, Jan Polisensky, and Roman Konecny, Lakmoos builds data models that simulate how target groups make decisions. The platform replaces human survey respondents with data models that reflect the dominant opinion of the selected target groups. In March 2024, the startup has secured €300,000 in a pre-seed round from Presto Ventures.
In the Startup of the Day column, Kamila Zahradnickova, co-founder of Lakmoos, shares the details about the startup’s idea, its product, and future plans.
The Startup of the Day column on AIN is dedicated to tech projects from all sectors that originate from the CEE countries. If you would like to introduce your project, please fill in the questionnaire.
Tell us about your startup. How does it work?
We create data models that simulate how people make decisions. Lakmoos uses our proprietary AI to create precise copies of target groups that companies can then interact with anytime. Unlike real people, our digital respondents never get tired or annoyed.
How did you come up with the startup’s idea? What was the reason/motivation behind it?
Before founding Lakmoos, I was quite involved in corporate research as a behavioural scientist. I helped shape campaigns for Nestle and Ogilvy, as well as NGOs like The Nature Conversancy, and I was just flabbergasted by the amount of guesswork that was deemed acceptable. So I started wondering about where the friction was – basically, why companies don’t simply run proper testing with their target groups.
Then I met my future co-founder Jan, shared my frustrations, and we started thinking about how to bring voice of the customer into any meeting. Initially, I covered the science behind measuring minds, Jan leveraged his pioneering research to build our layered AI, and our third co-founder Roman developed the first version of the Lakmoos app.
How long did it take to reach the prototype or MVP? What did you encounter?
Right after founding the company in June, we hired 20 people to help us develop the core of the product over summer. While everyone was on vacation, we created 14,000 lines of code.
When exactly did you launch your product? Or when the launch is planned?
We founded the company in June 2023 and released our first public demo in October 2023. It’s not available for enterprise use.
Tell us about the startup’s business model. How do you monetize your product?
Lakmoos is a SaaS product, meaning that after the implementation month, there are monthly fees based on the amount of users.
What are your target markets and consumers?
Right now, we’re focusing on five industries in the EU and US. Our model is layered and modular, meaning that it’s getting built step-by-step.
This means that banking layer can interact with telco layer, as financial behaviour is predictive of how much people spend on their phone credit. Using these optics, we chose industries that have enough data and also have a strong need to test their markets.
If the startup has already launched the product, what are the results: metrics, income, or any clear indicators that can be evaluated.
Our demo has been co-created with seven European banks, now we have our first paying enterprise customers.
What about your team? How many people are working in the startup? If you’re looking for new employees, indicate whom exactly.
There have been around 20 people in our team since the start. Most of the team are AI experts, data scientists, developers and methodologists. Right now, we’re hiring a sales team.
Have you already raised any investments? Provide us with more details on each funding round: the amount, investors, the purpose of the investment.
Yes, we first took on angel investment €210,000 in total. Right now, we raised our pre-seed of €300,000 with Presto Ventures to scale our sales with test kits to: banking, automotive, telco, energy, and healthcare.
What’s next? Tell us about your future plans.
We aim to onboard 13 new clients by the end of the year, mainly from banking, automotive, telco, energy, and healthcare. In addition, by 2030, we wants to transform 20% of traditional market exploration, saving $30 billion in exploration costs and 35 billion hours of fieldwork globally.
