She taught herself to code from a YouTube video, lectured at Stanford before she could legally rent a car, and now she wants to make AI tell you when it's guessing.
When a model is wrong in a hospital, a court, or a bank, "oops" is not an acceptable answer.
Miriam Haart runs ActionAI, a company with an unglamorous mission and a very large one. Most of the AI industry is racing to make models that do more. She is fixated on a quieter question: can you prove what the model did, and can you trust it where being wrong is not an option. ActionAI builds infrastructure for safe, auditable AI in the places that cannot afford a confident hallucination - finance, banking, insurance, manufacturing, supply chains, and the legal and judiciary systems.
The product has a name that doubles as a thesis. ActionAI calls its signature mechanism "Explainable Exceptions," or ExEx - a way to flag uncertainty and route it to a human instead of letting a language model bluff its way through a high-stakes decision. The company maps data across the full AI stack so each step can be tested and traced, runs real-time monitoring to catch failures as they happen, and handles edge cases fast in production. The pitch is almost contrarian for 2026: less magic, more accountability.
"AI is handling increasingly complex tasks with highly sensitive or personal data without any sufficient oversight or accountability," Haart has said. Her read on the market is blunt. Enterprises, she argues, have accepted a bad trade - deploy AI now, swallow the unreliability later. ActionAI exists to refuse that trade.
The thesis found money. In April 2026 the company announced a $10 million seed round led by UAE-based investors, with offices spanning New York and Tel Aviv. At 26, Haart is CEO and head of AI, paired with a leadership bench that includes a CFO out of Ernst & Young and a VP of engineering with two decades of R&D behind him. The reality-TV alumna is now the technical center of gravity at a company telling banks how to trust their robots.
Enterprises are facing the dichotomy of implementing AI while accepting the unreliability which goes alongside it.
At 13, Haart did not know what coding was. So she typed "how to make an app" into YouTube, followed along, and built one called Ateres. She could not read the code that made it run. That gap - between not understanding and shipping anyway - turned out to be the whole personality of her career. Curiosity first, mastery later, fear never.
She grew up in an ultra-Orthodox community in Monsey, New York, after an early childhood in Atlanta. She finished high school two years early, landed at Make School in San Francisco in 2016, and promptly won its Best iOS App award. Then came a run of apps that read like a teenager trying on entire industries for size: Recyclable, which used image recognition to check if something could be recycled; Blaze, for biking routes; Brows, which matched beauty products to skin tone by photo; Norma, a women's health tool that became a finalist at a Paris startup competition.
Stanford followed. There she did something rarer than getting in - she got hired to teach. At 19, Haart became the youngest lecturer in the university's Computer Science department, running a class on virtual reality development for students who were, in many cases, older than she was. She interned at Stanford's AI lab, built Cardinal Connect for fellow students, and in 2020 co-founded the delivery startup Eazitt as chief product officer before graduating in 2022.
A partial inventory of what she built before most founders write their first line of code. Some became startups. All of them taught her to ship.
Her talk, "The SITT Test: Are You Really Thinking Freely?", takes the fundamentalism she grew up around and asks an uncomfortable question - whether the rest of us, tech included, just trade one set of unquestioned rules for another.
Millions met her on My Unorthodox Life, the 2021 Netflix series about her family leaving an ultra-Orthodox world. She used the spotlight, then walked toward the harder thing - building reliable machines instead of being watched.
At 19 she was Stanford CS's youngest-ever lecturer, teaching virtual reality. The detail that matters: she was younger than her students, and taught anyway.
Profile compiled from public sources, interviews, and ActionAI press materials.