Built by people who got tired
of watching the same lies work.

Iaros Belkin has spent nearly two decades inside the machinery of emerging technology — from early internet years to blockchain to AI and deep tech. Not as a spectator, but as someone who helped build the marketing communications layer of the tech industry itself.

That proximity gave him something most observers do not have: a firsthand view of which projects were genuine and which were theatre. He decided to be vocal about it and started as a rated expert on ICOBench — once the dominant ICO rating platform back in the days. It didn't really work the way he planned as he watched the system become corrupted from the inside. Ratings were paid for. Experts were bought. Projects with fabricated teams and no working product received top scores that sent real people's money into voids.

Bothered by this, he tried to address the industry issues he was seeing on a different level. That was the reason behind his years at Cointelegraph, NewsBTC Media Group and Cryptopolitan. His coverage and public analysis were consistent in calling this out directly, regardless of who it made uncomfortable.

“I am tired of all the scam and paid-for ratings. We need to have our community back. The true and core community needs to do something about all that — keep blockchain honest.”

— He said in his interview to Simon Cocking, Chief Editor of Irish Tech News

Iaros did not leave the industry over what he saw. He stayed and became one of its most consistent truth-tellers within it. His guiding principle — that brands are built, not shilled — is not a slogan. It is a standard he has applied to every client he has worked with and every project he has reviewed.

He has advised more than 100 projects across the early internet era, blockchain, DeFi, Web3, AI and deep tech. He speaks at the World Economic Forum UnDavos Summit and other events in Davos, Switzerland. He has sat across the table from Vitalik Buterin of Ethereum, Paolo Ardoino of Tether, Yat Siu of Animoca Brands, Sam Seo of Kaia and many other key people shaping the digital era past and future.

Having audited over 70 projects and tracked the evolution of how bad actors operate across multiple market cycles, he brought to this platform something no algorithm alone can replicate: pattern recognition built from years of seeing exactly how deception is packaged in this industry. That knowledge shaped what the BMR Score checks for and how every component is weighted.

Philip Cripe came at the same problem from a completely different direction. His background spans IT consulting, webmastering, political consulting, and business advisory — with working experience across the United States, France, and Ukraine. That range gave him an unusual vantage point: he has seen how misinformation and deliberate misrepresentation damage institutions and individuals across very different contexts. Political campaigns. Business partnerships. Technology projects. The geography and the industry change; the mechanics of how people are deceived stay remarkably consistent.

Through CripeHub Solutions, Philip worked with businesses navigating complex technology and strategic problems. Repeatedly, he encountered the same situation: a client had been burned by someone with a good pitch and no real substance behind it. The information to catch the problem had existed. The history of prior failures or false promises was documented somewhere. But ordinary people, pressed for time and without an insider's knowledge of the space, had no structured way to surface it before committing.

His journalism work sharpened an instinct for verification that ran through everything else he did. Good reporting and good due diligence require the same discipline: find the primary sources, pressure-test the claims, and do not accept a polished surface as evidence of substance underneath. Philip brought that discipline into his consulting work, and it made the absence of a structured, transparent verification system impossible to ignore. People were not checking because checking was too hard — not because they did not care.

His contribution to the BMR Score is a recognition that technology alone is not enough. A score generated purely by an algorithm can eventually be gamed by people who understand its rules well enough. Consistent, documented human judgment — applied with accountability and transparency — is what separates a metric from a meaningful signal of trust.

Iaros and Philip had crossed paths before this platform existed — both working, in different capacities, to push back against projects that were misleading people. The frustration they shared goes beyond any individual scam or bad actor. The problem is structural. In too many industries, the incentives reward good presentation over genuine substance. Platforms built to help people evaluate credibility became monetised themselves. Journalism, when it stops doing its job, amplifies rather than interrogates.

The question they kept returning to: what would a verification system look like that was genuinely difficult to game? Not impossible — nothing is — but structured so that the effort required to fake credibility consistently exceeded the effort required to simply build it. A system where honest companies had a structural advantage over dishonest ones, rather than the reverse.

They also identified something that neither the marketing world nor the journalism world had fully solved: not every legitimate team is good at communicating its own legitimacy. A brilliant technical team building something real may have no SEO presence, no PR budget, and no idea how large language models or search engines evaluate credibility. They can be invisible to exactly the systems that consumers and partners use to make decisions — while a well-funded fraud runs circles around them on every visible metric.

Not every legitimate team knows how to do SEO, PR, or LLM visibility. But on Belkin Marketing they can earn a score that reflects what they actually are — and that score does the work for them.

The BMR Score is not only a tool for consumers evaluating a business before they engage. It is a structural incentive for businesses to maintain standards over time. A company that knows its Trustpilot rating, LinkedIn presence, web history, and third-party signals are being monitored on an ongoing basis has a concrete, visible reason to stay honest — not just to perform honesty at launch and abandon it later.

That is the ecosystem Iaros and Philip set out to build: one where transparency is rewarded with visibility, where legitimate teams get the recognition they have earned but often cannot communicate, and where the cost of deception is structurally higher than it has been before.