Hello Entropic team! Thank you for the time to share more about your project with us. We’re excited to learn about your journey, could you briefly introduce what you’re working on and what problem you're solving?
At Entropic, we're solving a critical problem in multi-omics data (e.g. genomics, transcriptomics, proteomics) research: current bioinformatics tools are too complex and fragmented for most researchers to use effectively. Today, analyzing any omics data requires specialized programming skills and juggling multiple disconnected tools, which creates a massive barrier for biologists and medical researchers.
Our software democratizes these analyses by integrating into a single, AI-powered platform with an intuitive interface. Researchers can now conduct sophisticated analyses in hours instead of weeks, without any programming knowledge.

How do you envision your platform transforming the day-to-day work of life science researchers, and what sets it apart from the bioinformatics tools they currently rely on?
‍Our platform transforms researchers' daily work by flipping the time ratio – instead of spending 70 % of time on technical setup and 30 % on analysis, researchers can focus primarily on scientific insights.
What sets us apart is (1) Intelligent automation – our AI automatically selects optimal analysis parameters, eliminating the need for programming expertise. Also, (2) edge computing accessibility – sophisticated analyses become available to smaller labs without expensive computational infrastructure.
The result? A researcher at any level can run complex analyses that previously required a dedicated bioinformatician, and discoveries that took months now happen in days. We're enabling entirely new research possibilities for the broader scientific community.

Who do you imagine will benefit the most from your platform?
We see three key groups benefiting most:
1) Academic research institutions – Universities and research centers where biologists and medical researchers need genomic (or any type of omics data) analysis but lack programming expertise. They're our primary target because of the clear accessibility gap. 
2) Biotech startups and mid-size companies – Organizations that need competitive analytical capabilities but can't afford dedicated bioinformatics teams.
3) Clinical and diagnostic labs – Healthcare institutions implementing precision medicine who need reliable, standardized genomic analysis workflows for patient care.
The common thread is researchers with valuable biological questions and data, but without the computational resources or expertise that larger pharmaceutical companies or major research centers possess. 

What inspired you to take your idea beyond academia and turn it into a startup?
The inspiration came from a perfect convergence of opportunity, timing, and the right team. During our work at the Ruđer Bošković Institute, we repeatedly witnessed great researchers struggling with the same frustrating issues – spending more time wrestling with bioinformatics tools than actually doing science.
We had the right colleagues with complementary expertise in bioinformatics, AI, and software development, and we realized we could solve this problem. The timing felt perfect – genomic data generation is exploding, AI technology has matured, and there's a clear market need for accessible solutions.

Have you had any early reactions or feedback from researchers or institutions that helped shape your direction?
‍Absolutely. We knew there was a real need for this solution from our daily interactions with fellow scientists at the Institute. More formally, we've established partnerships with a research institution and are currently in beta testing phases with a few key other bioinformatics partners, so we'll be presenting our current platform to them soon to gather comprehensive feedback.

What’s the story behind how the Entropic team came together? Was there a particular moment when you realized, “We should turn this into a company”?
Mateo Čupić: As I mentioned earlier, it was really about the right moment and timing aligning perfectly. There was an EU-funded program specifically designed to encourage young researchers like myself to launch startups related to our PhD research fields.
Since I was already working extensively with bioinformatics during my doctoral studies, along with molecular biology and data analysis, this opportunity seemed like a perfect match. My co-founder brought complementary expertise in machine learning and AI, which was crucial for the advanced analytical capabilities we envisioned. The timing couldn't have been better – I had the domain expertise, he had the AI knowledge, the technical challenges were clearly defined, and there was institutional support to make the entrepreneurial leap.

Why did you choose the name 'Entropic' for your startup? Does it carry a deeper meaning related to your work or philosophy?
‍The name 'Entropic' comes from the concept of entropy, which we felt perfectly describes our field. In bioinformatics, we're constantly dealing with enormous amounts of complex, seemingly chaotic biological data – genomic sequences, protein interactions, metabolic pathways - that appear random and disordered at first glance.
But just like in thermodynamics, there's underlying structure and meaning within that apparent chaos. Our mission is essentially to find order and extract meaningful insights from this biological entropy.

How has your experience in the DDAccelerator program influenced your journey so far? Can you share any highlights or insights?
The program has been incredibly valuable for us since we had excellent, engaging and educational sessions that really broadened our perspective on building a startup.
Working one-on-one with mentors was particularly impactful – the goal-setting exercises and active problem-solving sessions actually shifted our way of thinking about the business.

Would you encourage other startups to join DDAccelerator? What worked well for you, and what could be even better?
Absolutely, we would recommend DDAccelerator to anyone, especially those in the early stages of building a startup.
What worked particularly well was the combination of structured learning sessions and mentorship – it gave us both the theoretical framework and practical guidance we needed.

‍Thank you to the Entropic team for sharing your story and vision with us. We’re looking forward to seeing your progress and rooting for you as you bring simplicity and speed to the world of bioinformatics.