ActionAI raises $10M seed to fix enterprise AI’s trust problem and power reliable automation

Enterprises are pouring money into AI, but many still don’t trust what the systems produce. That tension is starting to shape the next wave of startups. ActionAI is stepping into that gap with fresh capital, announcing a $10 million seed round backed by UAE investors, with a focus on making AI dependable enough for high-stakes use.

The company is led by Miriam Haart, a Stanford-trained engineer and former computer science lecturer who some may recognize from the Netflix series My Unorthodox Life. Her pitch is straightforward: companies want AI to run critical operations, yet too many systems still produce errors, biases, or outright false outputs. That risk has slowed adoption where accuracy matters most.

The data tells a similar story. A global study by KPMG found that 66% of employees already use AI at work, yet more than half do so without verifying its accuracy. Mistakes are common. At the same time, research from McKinsey & Company suggests that most enterprise AI projects never move past pilot stages. The barrier is less about capability and more about trust.

As AI Errors Rise, ActionAI Raises $10M to Deliver Reliable Enterprise Automation

ActionAI is building its business around that exact problem. Its system tracks data across each layer of the AI stack, aiming to catch issues early and show where things break. The idea is to make failures visible instead of hidden, giving teams a clearer path to fix them before they reach production.

One piece of the platform focuses on what the company calls “Explainable Exceptions,” a framework that brings human review into the loop when something goes off track. Rather than letting a system push through questionable outputs, the model flags the issue and surfaces an explanation. That approach is meant to limit hallucinations and provide a record of how decisions were made.

The company has built monitoring tools that watch systems after deployment, tracking shifts in performance as new data or instructions come in. When something drifts, the system is designed to catch it in real time. For industries where mistakes carry real consequences, that kind of visibility could make the difference between cautious experimentation and full adoption.

The timing aligns with growing pressure on enterprises to get more value from AI. Many companies are dealing with inefficiencies that eat into operating costs, and automation offers a path to trim those losses. Yet there is still hesitation around handing over critical processes without clear safeguards in place.

“AI is handling increasingly complex tasks with highly sensitive or personal data without any sufficient oversight or accountability,” said Miriam Haart, CEO of Action AI. “ActionAI makes AI accountable from day one. Beginning with the initial data inputted, we review, fine-tune and secure the information which underpins an AI system. From there, our reliability architecture prevents AI vulnerabilities well before they reach production. Which enables AI automations with transparency and trust.”

Haart frames the current moment as a trade-off many companies are forced to accept: move forward with AI and live with its flaws, or hold back and risk falling behind. Her argument is that neither option works in the long term.

“Enterprises are facing the dichotomy of implementing AI while accepting the unreliability which goes alongside it. 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 startup is targeting sectors where errors are costly, including finance, manufacturing, retail, insurance, logistics, and legal systems. In those environments, a single incorrect output can trigger real financial or legal consequences. That reality has kept many organizations cautious, even as interest in AI continues to climb.

ActionAI’s bet is simple: if trust becomes the missing layer in enterprise AI, the companies that solve it could shape how the technology is deployed at scale.