Probably raises $9M to build a more reliable kind of AI

Probably, an AI safety and reliability startup, has secured $9 million in Series A funding to develop technology aimed at eliminating hallucinations and factual errors in artificial intelligence systems. The company is focused on creating AI that matches the accuracy standards of traditional deterministic software, addressing one of the most pressing challenges in modern AI deployment.

Probably's Series A funding round represents a significant investment in solving AI reliability—a critical issue as enterprises increasingly adopt AI systems for mission-critical applications. The startup's focus on preventing hallucinations, where AI models generate plausible-sounding but false information, directly targets a major concern limiting AI adoption in regulated industries including finance, healthcare, and legal services. By developing verification mechanisms and accuracy guarantees, Probably aims to bridge the gap between current AI capabilities and the reliability standards required for high-stakes deployments.

• Regulatory compliance: More reliable AI systems could accelerate adoption in heavily regulated industries where accuracy is non-negotiable
• Enterprise confidence: Reducing hallucinations and factual errors builds trust in AI deployment at scale across organizations
• Competitive differentiation: Reliability-focused solutions position Probably as a critical infrastructure layer in the AI ecosystem
• Safety standards: The funding signals growing market recognition that AI safety and verification tools are essential business investments
• Deterministic-level accuracy: Achieving accuracy parity with traditional software could fundamentally shift how organizations approach AI implementation

The emergence of companies like Probably highlights a fundamental shift in AI industry priorities. As generative AI becomes ubiquitous, the ability to guarantee accuracy and prevent hallucinations has moved from a nice-to-have to a business imperative. Organizations deploying AI for customer-facing applications, decision-making processes, or sensitive operations require ironclad assurances about output reliability.

This $9 million investment demonstrates investor confidence that specialized solutions addressing AI hallucinations represent a substantial market opportunity. With enterprises spending billions on AI infrastructure, tools that ensure reliability could become as essential as the foundation models themselves, potentially opening a new category of mission-critical AI infrastructure companies.