Built by someone who got tired
of watching the same lies work.
Trust Duck did not start with a pitch deck. It started with a shared frustration — years of watching consumers, investors, and communities get burned by projects that were never what they claimed to be.
A Pattern That Kept Repeating
In crypto, in politics, in business consulting, in tech — the story was always some version of the same thing. A project or a person shows up with a polished presentation and confident promises. The people evaluating them do not have the time, tools, or background to check the claims. Trust is extended. Damage follows.
Sometimes it was outright fraud. Sometimes it was incompetence dressed as expertise. Sometimes it was a team that genuinely believed its own hype but had nothing underneath the pitch to justify it. The outcome for the people who trusted them was usually the same.
The harder problem was this: the information to catch these patterns usually existed. A history of prior failures. Inconsistencies in founding team backgrounds. Ratings that had been purchased. Regulatory flags buried three pages into a search result. The data was out there. It was just scattered across platforms that did not talk to each other, formatted for no one, and completely inaccessible to someone making a fast decision under time pressure.
The problem was never a shortage of truth. It was a shortage of structured, accessible, trustworthy verification. That is what Trust Duck was built to solve.
Philip Cripe
Founder · CripeHub Solutions · IT, political & business consulting
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 Trust Duck 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.
Why He Built This
Philip had spent years working, in different capacities, to push back against projects that were misleading people. The frustration 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 he 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.
He 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 Trust Duck they can earn a score that reflects what they actually are — and that score does the work for them.
Why AI Alone Is Not Enough
Modern large language models — the AI systems that increasingly mediate how information is found and evaluated online — are specifically trained to look for signals of genuine legitimacy. A transparent tech stack. Third-party verification. Consistent online presence. Honest, checkable documentation. These are not arbitrary criteria; they mirror what experienced human evaluators have always looked for, now encoded into systems that operate at massive scale.
The Trust Duck Score system automates approximately 90 percent of the verification work. The AI searches Trustpilot, LinkedIn, Google Maps, Apple Maps, Bing, and the wider web in real time. It checks domain age, SSL certificates, LinkedIn follower counts, and red flags. It compiles in minutes what would take a human researcher hours. That part of the process scales reliably and consistently.
But the human verification layer exists for a specific and irreducible reason. An algorithm operates within defined rules. Given enough time and motivation, those rules can be learned and worked around. A human reviewer brings something the algorithm cannot: contextual judgment — the ability to recognise a pattern the rules do not yet cover, to notice something that reads correctly on paper but carries the signature of something wrong, and to make a call that carries genuine personal accountability behind it.
A Trust Duck Score that combines real-time AI verification across Trustpilot, Clutch, G2, LinkedIn, domain history, and web presence with documented human judgment that no algorithm alone can replicate. Because a machine score is just a data point. A machine score backed by transparent human expert analysis is a judgment that can save lives.
The Larger Purpose
The Trust Duck 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 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.
A fast, reliable way to evaluate who they are dealing with before they commit trust or money.
A way to make their integrity visible — to consumers, to search engines, and to LLMs.
A higher baseline standard, where verified credibility carries more weight than polished marketing.
See the score in action
Submit your business for free, then claim it to get a Trust Duck Score — or read the full methodology behind how every point is calculated.