We spent decades studying human behavior. Then we built the tools we wished we'd had.
EmpathixAI was founded by PhD social scientists who spent careers facing the same fundamental problem: the methods we had to understand human behavior hadn't meaningfully changed since the 1950s. So we built something new—not by chasing AI trends, but by applying decades of scientific expertise to solve the problems we knew firsthand.
This company started with a frustration, not a business plan.
Between us, we hold PhDs in anthropology and political science. We've spent decades—across academia and the public sector—studying how and why people behave the way they do, from Russia and Mexico to the United States and many places in between. We've run multi-year ethnographic studies and large-scale NSF-funded survey projects. We've done the work.
And the entire time, we faced the same tradeoff every researcher in human behavior faces.
The methods that are genuinely good at understanding complexity—the depth of meaning, the differences in interpretation, the real texture of how people experience their lives—those methods are primarily one-on-one interviews conducted by trained researchers. They produce extraordinarily rich data. And they don't scale. You can interview 30 people with real depth, but you can't interview 3,000. That means the findings are powerful but statistically fragile.
So for decades, the field has relied on surveys for anything that needs to be representative. But we've been using them for questions they were never designed to answer: questions about meaning, experience, motivation, identity. We've done this not because it works, but because nothing else scaled.
The methods that genuinely understand human complexity don't scale. The methods that scale were never designed for complexity.
For decades, the field chose. We were built to refuse that choice.
We tried the cheap approach. It failed—and the reasons why are the reason CultureChat exists.
The Assistant Problem
We started where everyone starts: we tried prompting existing models. But these models are trained to be assistants—to give information and provide help. Behavioral interviewing requires the opposite: eliciting information, sitting with ambiguity, and listening. You can't prompt your way past the fundamental architecture of a model optimized to be helpful.
The Internet's Bad Advice
Large language models have absorbed the entire internet, including an enormous amount of bad interviewing advice. They converge toward practices that are intuitive but scientifically wrong—the kind that sound reasonable in a UX blog but make a methodologist wince. Existing models produce interviews that feel conversational but are methodologically shallow.
"So we went the harder direction."
We collected an enormous proprietary training set and trained our own model. It worked better than we expected.
We didn't spend $300 million teaching a model English. We started with the language capabilities of existing foundation models and trained on top of them—using a proprietary dataset built specifically to teach a model how to conduct behavioral elicitation interviews the way a PhD-trained researcher would.
It worked. And it worked dramatically better than we anticipated. The model could hold fully dynamic interviews—not scripted conversations with a few follow-ups, but real interviews that adapted to each respondent, followed threads as they emerged, went deep where depth was needed, and exercised the kind of qualitative judgment that we'd spent our careers developing. That training process is what became CultureChat.
The Trust Surprise
People opened up faster and more honestly than we expected. The text-based format preserved anonymity, but the adaptive conversation built trust. Without a visible interviewer, social desirability bias lessened.
Contextual Fluency
The model moved between contexts with a fluency that would take a human researcher months. Trained to conduct rigorous interviews, it could bring vast background knowledge to bear on any topic.
We built CultureChat to solve a research problem.
The market showed us it was a much bigger need.
We started bringing CultureChat to market to see whether the research and insights industry would embrace it—and what we found was a need that went far beyond better interviews.
The teams we worked with weren't just struggling with data collection. They were struggling with the entire research lifecycle. They needed to make high-stakes decisions—product launches, brand repositions, market entry strategies—and the evidence supporting those decisions was thin, fragmented, or both. The skill to design rigorous research, to analyze complex qualitative data scientifically, and to produce findings that could withstand real scrutiny—that expertise was rare and expensive.
So the platform grew. We started building our decades of scientific expertise into tools that could bring that rigor to anyone who needed it. The AI Research Assistant—a system that analyzes full datasets with distributional methods. Relay—survey software that's scientifically opinionated. Evidence graphs that make every finding auditable. Institutional memory that connects research across projects and time.
Each of these grew from the same conviction: the decisions organizations make about people deserve better evidence than the industry has been providing. And the science to produce that evidence shouldn't require a PhD to access—it should be built into the platform itself.
A science company that builds the tools to do better science.
EmpathixAI is a full-cycle, global market research platform—from study design to fielding to analysis—built to deliver the kind of evidence that high-stakes decisions require. CultureChat conducts behavioral interviews at representative scale. Relay handles surveys with scientific rigor when surveys are the right tool. The Research Assistant reasons across all of it with the full analytical capability of a PhD behavioral scientist who has read every interview, remembers every finding, and shows all their work.
We are not an AI company that wandered into research. We are scientists who built AI to do better science. Everything about this platform reflects decades of expertise in understanding human behavior, embedded in tools that make that expertise accessible at scale.
Scientific Rigor
Built by PhDs to solve the problems we knew firsthand. The platform is deeply opinionated about methodology.
Global Scale
Serving teams across industries—from consumer brands and healthcare to financial services and public policy.
AI for Science
We didn't chase trends. We applied decades of scientific expertise to build AI that solves real research problems.
Leadership
EmpathixAI is led by scientists who have done the work.
