Professional.me, founded by Ryan Adams, is rethinking how organisations make talent decisions in an era overwhelmed by data but lacking meaningful insight. In this exclusive interview, Adams shares how his background in intelligence and cybersecurity shaped the company’s approach to replacing traditional resumes with evidence-based workforce intelligence, enabling faster, more accurate hiring and talent development across modern enterprises.

[For more news, click here]

1. Every startup begins with a moment of insight. What problem or experience first inspired you to build this company?

We spent years in intelligence and cybersecurity building systems that find signal in massive amounts of noise. Our last company, OryxLabs, processed over 10 billion events a day to detect threats across critical infrastructure. It was acquired by EDGE Group.

When we started looking at how enterprises make talent decisions, we recognized the same problem with worse tools. In cybersecurity, no one would make a critical decision based on a keyword match. But that's exactly how most organizations hire, retain, and develop their people. Resumes and job descriptions are the inputs, and those inputs are fundamentally broken. They don't capture what actually predicts whether someone will succeed in a specific role, team, or culture.

The insight was that this isn't a process problem. Everyone in the space was building better tools to process the same broken inputs. We asked a different question: what if we replaced the inputs entirely?

We actually started by building a niche job board. It didn't take long to realize how broken the foundation was. The data was terrible. The matching was superficial. And we thought: we've spent an entire career applying military intelligence and data analysis to high-stakes problems. Why not apply that same discipline to how organizations find, develop, and retain their people? That reframe unlocked everything. This isn't a recruiting problem. It's an intelligence problem. And we're intelligence people.

2. What is the core problem your company is trying to solve, and why do you believe existing solutions haven't fully addressed it?

The global staffing and recruitment market was valued at over $757 billion in 2023, according to The Insight Partners. A landmark Leadership IQ study tracking over 20,000 hires found that 46% failed within 18 months. And SHRM estimates each failure costs 50 to 200% of that person's salary when you factor in vacancy drag, lost productivity, and the domino effect when good employees follow them out the door.

The reason is structural. The entire industry is built on resumes and job descriptions, artifacts from a time when work was simpler and people stayed at companies for decades. They were never designed to capture what predicts success.

It doesn't matter who processes them. Humans can't extract information that isn't there. AI can't predict outcomes without relevant data. And now LLMs write the job posts and LLMs write the resumes, so every opening gets flooded with thousands of perfectly keyword-optimized applications that say nothing meaningful. Most of the industry is responding by building AI to process those same broken inputs faster. They're optimizing garbage throughput. We set out to replace the inputs entirely.

3. Your platform sits at the intersection of technology and innovation. Can you explain how your solution works in simple terms and what makes it technically unique?

The simplest analogy: traditional recruiting is like swiping on a dating app based on photos. What we do is more like getting a meaningful introduction from someone who's known both parties for years and understands the nuances that actually matter.

On the individual side, we build AI Advocates. Instead of a static resume, a professional's AI Advocate acts as a living career diary that tracks contributions, pulls in real evidence like published work, peer endorsements, and project outcomes, and understands their actual capabilities and potential. Social proof, not self-promotion.

On the employer side, we build tailored micro-LLMs for each organization. Instead of generic job descriptions, these models learn what actually drives performance in that specific environment by ingesting performance reviews, promotion patterns, retention data, and 360 feedback. They learn your real culture, not your careers page marketing.

The reason we use micro-LLMs instead of a monolithic model comes down to context precision. A great software engineer at a fintech startup looks nothing like one at a government digital transformation project. One model averages across those domains and loses the signal. Specialized models trained on specific organizational contexts maintain that precision. When they interact, they're comparing compatible contexts rather than matching keywords.

Every piece of evidence gets weighted. Who said it, how would they know, and how recent is it. A government-verified credential carries more weight than a self-reported skill. A performance review from six months ago outweighs one from three years back. So when the system surfaces a recommendation, you can trace exactly why and how much confidence to place in it. That gives you explainability for boards and regulators, privacy by design for UAE compliance, and speed, because the system surfaces the right candidates the moment a role opens.

4. Can you share a real-world example or use case that best demonstrates the value your technology brings to users or businesses?

One enterprise client received 3,300 applications for a single role. Their existing system flagged hundreds as "qualified," but the vast majority were AI-generated applications with perfect keywords and zero substance. Our platform narrowed that to a defensible shortlist and saved them over 10 days of manual screening.

An engineering firm paying recruiters $15,000 per hire now posts roles directly through our system, saving that full cost every time. A manufacturer in Dubai cut 10 days of screening down to hours. Across the board: 87% less screening time and 25% faster hiring.

But the use case we find most interesting from a data perspective is internal workforce intelligence. We analyzed one enterprise client's data across 1,200 employees and surfaced 23 succession-ready candidates their leadership team didn't know existed. 14 were Emirati nationals currently one to two levels below the roles they could grow into. That's the kind of intelligence that turns Emiratization from a compliance exercise into a genuine talent development strategy.

We also hired three of our own team members using the platform. When you're a startup and every hire is make-or-break, you don't use your own product for critical hires unless you trust the intelligence completely.

5. What has been the most difficult challenge your team has faced since launching, and how did you overcome it?

Our roots are in building deeply technical products for technical users. My background is cybersecurity. We've built systems that process billions of events for engineers and analysts. Professional.me is just as complex under the hood, but the end users are CHROs and VP-level HR leaders, not engineers.

That required a fundamental shift in how we think about product design. The intelligence has to be sophisticated, but the experience has to feel effortless. An HR leader shouldn't need to understand micro-LLMs or evidence weighting to benefit from them. They should just get better answers, faster. That tension between technical depth and user simplicity has actually become one of our strongest differentiators.

We also faced the classic enterprise integration challenge. These organizations have invested millions in Oracle, SAP, Workday. If your solution requires them to rip that up, you've lost before you've started. So, we built Professional.me as an intelligence layer that integrates with what they already have. Their ATS, their HRIS, their workflows stay the same. We make them smarter. That positioning took iteration to get right, but it removes the procurement friction that kills most enterprise deals.

And building in the UAE right now means building through uncertainty. The current regional situation has reinforced something we already believed: organizations need better workforce intelligence precisely when conditions are volatile. When talent mobility increases, when restructuring becomes urgent, when leadership pipelines get tested, that's when evidence-based decision-making matters most. We designed our systems to be resilient and to help organizations be resilient too.

6. Your company operates in a rapidly evolving industry. What major trends do you believe will shape the sector over the next five years?

The first is that resumes and job descriptions are going to become functionally meaningless. When AI writes both sides of the equation, keyword matching becomes theater. The companies that recognize this early and invest in richer talent data will have a massive advantage. The ones that keep optimizing the old inputs will fall further behind.

The second is the convergence of workforce intelligence and enterprise AI infrastructure. Right now, most enterprises have sophisticated real-time systems for revenue, operations, and supply chain. But for their people, the most expensive asset on their balance sheet, they're still working from spreadsheets and annual reviews. That gap will close, and we believe micro-LLM architectures trained on organizational context will be the foundation, not general-purpose models that average across industries.

The third, specific to this region, is that nationalization programs will shift from quota-based compliance to intelligence-driven talent development. The organizations that can map internal capabilities, identify development paths, and surface succession-ready national talent with real evidence will meet their mandates while building stronger leadership pipelines. The data infrastructure to do that at scale didn't exist until recently. Now it does.

Underpinning all three trends is a reality the current regional situation has made harder to ignore: periods of uncertainty accelerate the need for workforce intelligence; they don't diminish it. When organizations face disruption, whether from market volatility, geopolitical shifts, or rapid restructuring, the cost of making talent decisions on gut instinct goes up, not down.

7. Looking ahead, what is the long-term vision for the company, and how do you hope it will reshape your industry?

The long-term vision is to become the intelligence layer for how talent and organizations find each other globally. Not another job board. Not another ATS. The system that sits underneath all of them and makes every talent decision, from hiring to retention to succession to restructuring, evidence based.

Our immediate focus is the UAE and MENA. The long-term fundamentals here remain strong: enterprises navigating Emiratization at scale, Vision 2030 workforce planning, and a regulatory environment that rewards innovation. The current regional situation doesn't change those fundamentals.

If anything, it sharpens the urgency. Organizations that can make faster, smarter decisions about their people during periods of uncertainty are the ones that come out stronger on the other side. We're in active conversations with some of the region's largest employers across real estate, telecom, and defense, and our commitment to building here is long-term. From there, we're deepening enterprise integrations and expanding across EMEA with the UAE as home base.

We've raised $4.6 million led by Raha Beach Ventures, we're part of Hub71 x Techstars Cohort 17, and we were selected for the MBRIF Innovation Accelerator. We've partnered with Microsoft to deliver talent intelligence within their enterprise ecosystem, and we're building integrations with Oracle and SAP. We won the GITEX Europe Award at the AI Everything Supernova Pitch Competition, the only MENA startup to win any category.

But the metric that matters most isn't funding or awards. It's outcomes. Over 600,000 professional profiles processed across Europe, the UK, and MENA since October 2024, including 110,000 verified Emirati professionals. The industry has spent decades trying to make broken processes faster. We're replacing the processes entirely.