How Maria Kolitsida Built Trust and Innovation As A Non-Technical Founder

Understanding why people behave the way they do has long been vital to improving safety and reducing risk in high-stakes environments like healthcare and the military. Behavioral analytics is often seen as essential for revealing these insights by uncovering the patterns and factors that influence decision-making, performance, and risk, but its shortcomings have become increasingly clear in recent decades.

Most traditional models can recognize surface-level patterns, but they often lack the depth to fully explain what drives behavior or predict how someone will react under pressure, making it difficult to apply in sectors where situational awareness and decision-making skills are crucial. Emerging tech like artificial intelligence promises to grant new insight into why people behave the way they do, but they require a certain fluency with advanced technology that many non-technical founders lack.

Entrepreneur Maria Kolitsida has overcome this challenge. She’s gone from a non-technical background to being the founder of Signal Fusion, a risk management platform that uses AI to support behavioral analytics in high-risk fields like healthcare and emergency services.

Unlike most tech founders, her background is in biology and biotechnology, with a career spanning corporate roles in pharmaceuticals, food science, and regulatory compliance. These experiences provided her with firsthand insight into the complexities of human behavior and the challenges organizations face in managing risk. Throughout these roles, she also deepened her understanding of technology, coming to recognize the transformative potential of AI — particularly causal AI — to address the shortcomings of traditional behavioral analytics.

“Even though I started in other fields,” she says, “I became increasingly intrigued by AI as I saw its potential to enhance behavioral science. Paying closer attention to how AI could be used as a tool to not just analyze but truly understand and improve human behavior in real-world applications led me to shift my focus and bridge the gap between science and technology.”

Learn more about how Maria’s non-technical background shaped Signal Fusion’s approach to improving risk management.

Maria Kolitsida: A Founder Focused on Human Behavior

After earning degrees in biology and biotechnology, Maria built her career at leading pharmaceutical and food brands — giving her firsthand knowledge of how large organizations operate in highly regulated, complex markets where decisions directly impact public health, safety, and compliance.

Maria has also worked with several early-stage startups, leading projects focused on human relations and crisis services. In these roles, she regularly tackled challenges that required a deep understanding of human behavior, risk management, and decision-making — including a careful examination of how behavioral analytics are used to measure and predict consumer behavior online.

As Maria examined these widely accepted analytics models, she noticed that many were either developed by tech-driven teams with a limited understanding of human behavior or relied on outdated scientific theories that hadn’t evolved with modern data-driven approaches — resulting in assessment tools that were either overly simplistic or lacked real-world applicability. Furthermore, many models depended on self-reported questionnaires or periodic evaluations, failing to capture the deeper factors behind cognitive strain and fatigue.

Recognizing this gap, Maria immersed herself in research and collaborated with experts across both disciplines, looking for a solution that would provide more precise, proactive risk assessment.

“My transition into technology and AI came from a need to improve people analytics by applying insights from neuroscience, cognitive and affective science, risk modeling, decision-making frameworks, and digital mental health,” she says. “I wanted to bridge the gap between research and practical application, building systems that are more accurate, less biased, and better at interpreting human behavior. My focus has been on using AI to turn this knowledge into actionable insights that improve risk assessment and decision-making.”

The solution, she found, lay in causal AI: a branch of artificial intelligence focused not just on identifying patterns but also on understanding their origins. Unlike traditional models that rely on static correlations, a hybrid model that integrates causal AI with scientific principles could reliably move beyond pattern recognition and achieve deeper, contextual insights into human behavior — allowing for a more effective system for assessing risk, identifying the true drivers of behavior, and improving decision-making in complex environments.

Developing an AI Platform Without a Technical Background

Encouraged by causal AI’s capabilities to uncover more than just surface-level insights, Maria began developing what would become Signal Fusion, a risk management platform designed to support high-stress industries like maritime operations, military, and emergency response services — fields where personnel face extreme pressure and make split-second decisions that can have life-or-death consequences. These conditions make personnel particularly vulnerable to stress, fatigue, and burnout — issues that, if unaddressed, can impair their ability to react, assess risks, and communicate effectively. As such, their cognitive resilience directly impacts the likelihood of compromised safety or operational failures.

To ensure Signal Fusion could accurately assess these risks, Maria trained its AI on principles from fields like cognitive science, decision theory, and psychology — disciplines that explore how people think, react, and make choices under pressure. She also ensured the system provided clear explanations for its behavioral assessments, allowing crew managers to see the reasoning behind each conclusion rather than just receiving an output that lacked context. Finally, she oversaw the design of an intuitive interface that would enable companies to easily integrate the tool into their workflows without disrupting them.

Signal Fusion works by leveraging voice-based psychometrics — a method that analyzes vocal and linguistic markers like tone, pitch, and speech rate to assess cognitive and emotional states. The platform collects this data through periodic check-ins, where personnel provide verbal answers to tailored questions based on their role, tasks, and work conditions.

By analyzing both what personnel say and how they say it (in high- and low-stress situations), Signal Fusion establishes a behavioral baseline for each crew member. Over time, as it gathers more data, the platform refines its ability to detect subtle deviations that may indicate stress, fatigue, or cognitive overload. This longitudinal approach allows Signal Fusion to build individualized risk profiles for each crew member, providing managers with insights into how their cognitive and emotional resilience changes over time.

In combining causal AI with voice-based psychometrics, Signal Fusion offers a proactive way to manage behavioral risks, giving organizations the information they need to keep personnel safe by identifying issues before they escalate to more critical incidents.

The platform has since gained recognition from both customers and investors. Techstars, a leading startup accelerator, recently accepted the project into its 2024 program. With their support, Maria gained access to funding, mentorship, and research tools that helped further refine and expand the platform.

Five Key Lessons from Maria’s Approach

Maria’s work in developing Signal Fusion provides a compelling example of how to bridge the gap between technology and human behavior.

Here are five key takeaways from her approach:

1. AI Should Learn with Humanity, Not Just from Humanity: Rather than designing a system that simply makes static predictions, Maria designed Signal Fusion to adapt to new information and changing contextual factors. She believes that AI should function as a continuous learning tool, evolving alongside its users instead of just analyzing past behavior.
2. Real-World Impact Starts with Understanding Human Behavior: By training Signal Fusion with causal AI, which focuses on understanding cause and effect, Maria built a tool that can reliably capture the nuance of human decision-making.
3. Transparency and Explainability Are Essential for AI Adoption: By making Signal Fusion’s decision-making process transparent, Maria helps users better understand and trust its insights. This clarity allows them to make more informed decisions, reducing uncertainty and ensuring usefulness in real-world applications.
4. Interdisciplinary Collaboration Drives Innovation: While technical expertise is essential, it’s not enough on its own. Maria integrated insights from fields like psychology and cognitive science to make Signal Fusion more practical and effective, showing how AI can benefit from both technical and non-technical perspectives to improve its accuracy and usefulness.
5. Seamless Integration Encourages Adoption: Maria firmly believes that usability and accessibility are just as important as technical capabilities. Because Signal Fusion’s interface is intuitive and user-friendly, teams can adopt it without added complexity.

Expanding AI to Tackle Real-World Challenges

As shown by Maria Kolitsida’s work with Signal Fusion, even a non-technical founder can bring a fresh perspective to AI development.

By combining causal AI with her firsthand experience in cognitive science, psychology, and decision theory, she’s built a tool that relies on proven scientific principles to support critical environments and help high-stakes organizations manage risk.

BDG Media newsroom and editorial staff were not involved in the creation of this content.