In the summer of 2023, I took the stage at the Reuters Momentum Festival and shared a simple yet key insight: Tinder had three core problems to solve: realness, respect, and relevancy. These weren’t just marketing buzzwords. They were the honest diagnosis of what happens when hundreds of millions of people rely on a platform to make consequential decisions about other humans, decisions that deeply affect their lives.

Two years after leading product at Tinder, I’m now building a company serving professional services, and I’m confronted with the exact same three challenges. Whether it’s dating or hiring a real estate agent, lawyer, or financial advisor, the fundamental problem is trust.

Why Tinder Was Never Just a Dating App

Many think of dating apps as entertainment, but they’re really trust infrastructure. Tinder isn’t just about swiping; it’s about helping people make one of the most emotionally significant decisions they’ll ever face: who to share their time and life with. The challenge wasn’t building a matching algorithm, it was about asking users to extend trust to strangers based on a handful of photos and a short bio.

At Tinder, the core issue was that the profile “felt too transactional.” People craved deeper dimensionality about who someone really was before taking the leap of trust. This insight revealed a universal truth: platforms mediating consequential human decisions must solve for realness, respect, and relevancy.

Every Professional Marketplace Faces the Same Trust Challenges

Consider the last time you needed a lawyer, a financial advisor, or a real estate agent in a new city. You probably asked a friend, searched Google, or even turned to a large language model (LLM) for recommendations. Yet, the trust you extended was often based on little more than star ratings, bland headshots, and generic copy optimized for SEO.

This is the exact problem I wrestled with at Tinder. On the other side of this dilemma are professionals who know their worth but struggle to prove it in a format that machines and consumers can trust. Without a clear, machine-readable representation of their real expertise, the best professionals remain hidden.

Realness: Making True Expertise Visible

At Tinder, realness meant going beyond photos and bios to add depth and authenticity. In professional services, the challenge is similar but more complex. Many top professionals have built their reputations through years of offline interactions whether successful deals or client outcomes that are invisible to AI systems and search engines.

The professionals who will thrive are those who figure out how to make their real track records machine-readable, honestly and structurally, without gaming the system. This transparency is what earns trust and drives recommendations in the AI era.

Relevancy: Matching Fit, Not Just Visibility

On Tinder, relevancy meant connecting people with shared intentions rather than just those nearby or statistically likely to swipe right. In professional services, relevancy is about fit, the right professional for your unique situation.

Today’s platforms rarely get this right. Directories rank by ad spend, review sites by volume, and SEO favors those who blog the most, not those who are the best match. LLMs, however, reason about fit differently, and professionals who structure their strengths so AI can understand and recommend them will win.

• Relevancy isn’t just about being found; it’s about being matched.
• AI recommendations prioritize nuanced understanding over simple popularity.
• Professionals must articulate their unique expertise in structured, machine-readable ways.

Respect: Building Platforms That Protect Trust

Respect at Tinder meant safeguarding users from harm because the worst experiences erode the trust the entire system depends on. In professional marketplaces, respect is structural and often lacking.

Most platforms profit by extracting value from the consumer-professional relationship. Lead generation companies sell the same consumer to multiple professionals. CRMs focus on maximizing revenue per contact rather than helping consumers find the right match.

These are transaction platforms masquerading as trust platforms. You can tell the difference by examining whose interests the product protects when conflicts arise. True trust platforms prioritize respect for both consumers and professionals.

Why the Moment to Build Trust Platforms Is Now

For two decades, search engines mediated the first interaction between consumers and professionals. That era is ending rapidly. Increasingly, language models are the new intermediaries, reasoning about fit and delivering single, natural language recommendations instead of lists of links.

When you ask an LLM 'Who should I work with?' it won’t give you ten blue links; it will offer one recommendation. The professionals who get recommended will be those whose realness, relevancy, and respect are legible to the AI systems doing the recommending.

Those who fail to adapt won’t be outcompeted on service or price, they’ll simply disappear from the conversation.

What We’re Building: Catchouse, the Trust Graph for Professionals

At Catchouse, we’re creating the trust graph for professionals. Starting with real estate, the highest-stakes, lowest-frequency decision most people make. The gap between how good an agent truly is and how they appear online is wide here, more than in many other industries.

But our vision is broader. Every professional industry needs a machine-readable trust layer. Every one is about to face the same challenges we solved at Tinder.

Keeping It Human in an AI World

AI doesn’t replace the deeply human moment when someone chooses who to work with for a consequential decision. Instead, it raises the bar on how trust must be earned, demonstrated, and made visible.

Professionals who stop optimizing for outdated signals and start building real, structured representations of who they are, and who they serve will define the next decade. Realness, respect, relevancy: these remain my guiding principles, just on a new stage.