From Reality TV to Reliable AI: Haart's ActionAI Raises $10M

NEW YORK and TEL AVIV, Israel – April 17, 2026

ActionAI, a startup aiming to solve artificial intelligence’s pervasive trust problem, announced today it has secured $10 million in seed funding. The round, led by prominent but undisclosed UAE-based investors, will fuel the company's mission to build a reliability infrastructure that makes AI auditable, accountable, and safe for mission-critical enterprise operations.

The company is founded by Miriam Haart, a Stanford-educated engineer and computer science lecturer who gained international recognition as a star on the Netflix hit series 'My Unorthodox Life.' Her venture places her at the intersection of celebrity, technology, and the urgent business need for trustworthy AI, tackling one of the biggest obstacles to the technology's widespread adoption.

The Enterprise AI 'Trust Gap'

As companies race to integrate AI into their core processes, a significant "trust gap" has emerged, stalling progress and costing billions. While a recent KPMG study shows that two-thirds of employees now use AI at work, that adoption is fraught with risk. The same study reveals that 58% of users don't evaluate AI outputs for accuracy, and 56% report that mistakes have arisen from its use.

This unreliability has tangible consequences. According to research from McKinsey & Company, a staggering 90% of enterprise AI use cases remain stuck in the pilot phase, unable to graduate to full-scale production. The bottleneck, McKinsey argues, isn't technical but human: a fundamental lack of trust.

This erosion of confidence is fueled by the well-documented flaws of current AI models, particularly Large Language Models (LLMs). Issues like inherent bias, security vulnerabilities, and "hallucinations"—where the AI confidently fabricates false information—are rampant. Some studies have found hallucination rates as high as 79% in certain models, a figure that is simply untenable for businesses operating in regulated or high-stakes environments. When companies stand to lose up to 30% of their operating costs to inefficiencies that AI could theoretically solve, the inability to trust the technology becomes a major economic roadblock.

An Unorthodox Founder's Path to Deep Tech

Navigating this complex challenge is Miriam Haart, whose journey to founding an AI infrastructure company is as unique as her public persona. While many know her from 'My Unorthodox Life,' which chronicled her family's transition from an ultra-Orthodox Jewish community, her background is deeply rooted in technology.

A computer science graduate from Stanford University, Haart also co-taught a course on virtual reality development in the university's CS department, making her one of its youngest instructors. Her early career included developing over 10 mobile applications and working as a product engineer at an AI firm. This blend of rigorous technical training and a high-profile media presence gives ActionAI a unique advantage: the credibility to speak to engineers and the platform to communicate a complex problem to a broader business audience.

"AI is handling increasingly complex tasks with highly sensitive or personal data without any sufficient oversight or accountability," said Miriam Haart, CEO of ActionAI, in a statement. "ActionAI makes AI accountable from day one." Her vision is to move the industry beyond the current dichotomy of either embracing unreliable AI or forgoing its benefits altogether.

Building an Infrastructure for Accountability

ActionAI's strategy is not to build another LLM, but to construct the foundational "reliability infrastructure" that allows enterprises to deploy any AI model with confidence. The company's technology is designed to provide oversight across the entire AI lifecycle, from the initial data used for training to the final output in a production environment.

The platform's core components target AI's most critical vulnerabilities. It begins by mapping data to every point in the AI stack, enabling granular evaluation and testing before deployment. Once live, its real-time debugging tools can instantly spotlight where a failure occurs, allowing for rapid handling of unexpected "edge cases."

A key innovation is a feature the company calls "Explainable Exceptions (ExEx)." This system is designed to directly combat LLM hallucinations by creating a human-in-the-loop process. When the AI encounters a query it cannot answer with high confidence, ExEx flags it for human review, providing context and explanation for the exception. This prevents the model from inventing an answer and ensures that critical decisions remain under human oversight. In the production stage, continuous monitoring tools automatically identify performance dips or mistakes triggered by new data, mitigating risk before it can impact operations.

"Enterprises are facing the dichotomy of implementing AI while accepting the unreliability which goes alongside it," Haart continued in her statement. "As AI improves, we need to ensure it can be trusted. This is what ActionAI is delivering: secure, transparent, reliable AI for mission-critical enterprise use-cases."

The $10 Million Bet on Mission-Critical AI

The $10 million investment, a substantial sum for a seed round, underscores a growing consensus among investors: the next major wave of AI value will be unlocked by companies that solve the problem of trust, not just performance. The backing from UAE-based investors also points to a global recognition of this challenge and the strategic importance of building dependable AI systems.

ActionAI is specifically targeting mission-critical industries where errors are not just inconvenient but can have severe financial, legal, and safety consequences. These include finance and banking, manufacturing, insurance, supply chain logistics, and legal systems. In these regulated sectors, the demand for auditable and explainable AI is not just a preference but a looming regulatory requirement, with frameworks like the EU AI Act setting a new standard for governance.

By providing a tech stack that ensures accuracy, safety, and compliance, ActionAI aims to give these industries the ironclad guarantees they need to move beyond pilots and automate core workflows. The goal is to transform AI from a risky, unpredictable tool into a reliable engine for minimizing operational costs, generating deeper insights, and driving long-term business transformation. The successful deployment of such systems could finally bridge the gap between AI's immense potential and its practical, trustworthy application in the real world.