In today’s AI race, companies are under intense pressure to move fast. But reliability remains a major barrier between AI prototypes and real-world deployment. Hallucinations, data leakage and other issues bring serious consequences. As AI systems take on more responsibility in critical workflows, strong safeguards, monitoring, and human oversight become essential.
The data reinforces this urgency. Hallucination rates ranged from 1.8 percent to 24.2 percent in December 2025, according to an ongoing industry leaderboard and 30 percent of companies report experiencing at least one negative consequence from AI inaccuracy.
After years working in AI, Gabriel Bayomi, Rishab Ramanathan, and Vikas Nair say fixing the reliability issue requires rigorous, repeatable testing and governance. Their company Openlayer provides an AI governance and observability platform designed to make advanced AI systems reliable, safe, and production-ready. The platform embeds oversight directly into how AI systems are built, deployed, and monitored on an ongoing basis.
The founders describe the framework using a civic metaphor: a “legislature” that defines behavioral rules, an “executive” that enforces them across environments, and a “judiciary” that evaluates edge cases when models drift off course. The result is a living governance system that evolves alongside the models it oversees.
That philosophy has resonated with enterprises operating at scale. Openlayer recently raised a $14.5 million Series A and counts companies such as eBay, UTMB Health, and Globo.com among its customers.
Cybersecurity firm Jericho Security uses generative AI to power enterprise phishing simulations and deployed Openlayer to ensure outputs remained reliable and compliant. With the platform in place, Jericho achieved pass rates above 95%, cut prompt iteration cycles in half, and avoided months of internal engineering work. In another case, a Fortune 1000 financial institution scaling from one to more than 10 trading models needed auditable oversight to satisfy regulatory requirements. Using Openlayer, the firm gained daily visibility into model performance across cohorts, achieved full coverage for monthly regulatory reporting, and avoided more than a year of custom tooling development.
Why Three Apple Engineers Decided Reliability Was the Real AI Bottleneck
The three founders met while working on separate AI projects at Apple and quickly bonded over a shared frustration that reliability, not raw capability, was the deciding factor between success and failure. In their spare time, they began building tools to systematically test models for output errors, fraud risks, and classification issues. After gaining early traction and acceptance into Y Combinator, they left their roles to focus on Openlayer full-time.
Under the hood, Openlayer allows teams to run AI systems through structured test suites. Any change gets evaluated to ensure it improves behavior rather than introducing regressions, while maintaining a clean, traceable version history.
Once a model goes live, Openlayer monitors according to the behavioral rules previously set up, but in a real-world environment. Instead of guessing how users might break a system, teams receive real-time alerts and granular diagnostics when models deviate from expectations. Issues discovered in production can be reproduced exactly in development, fixed with confidence, and redeployed without guesswork. Openlayer also provides a unified governance overlay for managing every AI system within an organization, whether it’s generative, traditional ML, audio, video, or text-based.
A key differentiator is accessibility. Openlayer’s interface allows non-technical stakeholders to define behavioral rules, monitor outcomes, and investigate failures without writing code. “Defining what success looks like for an AI system and evaluating whether it achieved its purpose, should not be limited to technical teams,” said Nair. “That responsibility should be shared across the organization.”
Where Openlayer Meets Real Enterprise AI Constraints
Openlayer joined the Comcast NBCUniversal LIFT Labs Accelerator in fall 2025, where the founders engaged directly with enterprise teams exploring AI pilots and partnerships. The program offered exposure to how a Fortune 100 company evaluates AI readiness, risk, and long-term scalability.
Looking ahead, the founders see regulation accelerating the need for platforms like Openlayer. From the EU AI Act to emerging frameworks in Brazil, Canada, and multiple U.S. states, governments are raising expectations around transparency, auditability, and risk management.
“Governance is moving from a nice-to-have to core infrastructure,” Ramanathan said. “The next phase of AI will be defined by how reliably we can deploy them. In a world where AI moves at the speed of compute, trust is the most valuable feature you can ship.”
