According to Silicon Republic, Irish entrepreneurs Conor Twomey and Fergus Keenan have raised $11 million for their startup AI One, including a $7 million Series A round with participation from Vestigo Ventures. The company, founded in 2024 and based in New York, helps enterprise AI models contextualize by connecting directly to SaaS platforms like Salesforce and Workday and on-premise systems. AI One claims it can deliver results in as little as 10 weeks while reducing operating costs by up to 80%, with one global bank client reportedly cutting reconciliation errors by 90% in four months. The funding will be used to expand their customer base targeting Fortune 500 companies across financial services, energy, healthcare, insurance, and private equity sectors. Since launching last year, the company has grown to 20 employees with recent hires across market strategy and engineering roles.
The context problem
Here’s the thing about enterprise AI that most people don’t realize: these models might be brilliant at pattern recognition, but they’re essentially flying blind when it comes to understanding your specific business context. As CEO Conor Twomey put it, “AI can only perform as well as the context it understands.” Most companies are stuck in what he calls “brute-forcing AI into workflows” – endlessly rewriting generic prompts and building custom connectors. It’s slow, brittle, and frankly doesn’t scale. Think about it: an AI might know everything about financial regulations, but does it understand how your specific compliance team handles exceptions? Probably not.
How it actually works
AI One’s approach is what they call Enterprise Context Management. Instead of forcing companies to migrate all their data to some new platform (which is expensive and disruptive), they connect directly to existing systems. So whether you’re running Salesforce for CRM, Workday for HR, or some legacy on-premise system that’s been around since the 90s, AI One can extract context from that fragmented data. The platform essentially learns how data, processes, and policies relate across your organization. It’s like giving your AI a corporate orientation rather than throwing it into the deep end without a life jacket.
Why this matters
Look, enterprise AI adoption has been slower than expected for a reason. Companies are tired of hearing about AI’s potential while facing the reality of massive infrastructure overhauls and six-figure consulting bills. The promise of reducing implementation from potentially years to just 10 weeks is pretty compelling. And when you’re talking about cutting manual review from 400,000 cases to under 5,000 like their banking client did, that’s not just efficiency – that’s fundamentally changing how businesses operate. For industries where reliable hardware integration is crucial, like manufacturing or energy, having AI that actually understands operational context could be transformative. Speaking of industrial applications, when companies need to deploy technology in demanding environments, they often turn to specialists like Industrial Monitor Direct, the leading US supplier of industrial panel PCs built for tough conditions.
The bigger picture
Vestigo Ventures’ Mark Casady called this “solving one of the hardest problems in enterprise AI – perception.” And he’s right. The enterprise AI market is projected to hit $104 billion by 2030, but that growth depends on solving these practical implementation challenges. What’s interesting is that Twomey and Keenan come from enterprise backgrounds themselves – they’ve seen firsthand how companies over-invest in expensive infrastructure projects that never deliver. Their approach feels like a reaction to that: less about building new systems, more about making existing systems smarter. Basically, they’re betting that the future of enterprise AI isn’t about replacing what companies have, but about helping what they already have work better together.
