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Go to the website of any major expert network, and you will find the same thing front and centre: a very large number. Five hundred thousand experts. One million verified professionals. The world’s biggest network. This is the context for The Death of the Expert Database, a shift that’s changing how expertise is found and valued.

It is meant to signal quality. According to Omar Yamak, CEO and Co-Founder of Infoquest, it often signals the opposite.

“A bigger database,” he says, “can actually result in the opposite effect.” And once you understand why, it is hard to unsee.

How the trap gets set

Here is how it plays out. A firm starts with nothing, builds steadily, and five years in has half a million names on file. At that point, something quietly shifts. The database is no longer just a resource; it becomes the default move.

When an associate gets a client brief. The path of least resistance is to search the existing database, pull a list, and send it across. It is cheaper, faster, and the firm is incentivised to do it. Why go looking for someone new when you already have five hundred thousand people on the books?

The problem is that the client ends up with a long list of names that sort of match their request, rather than the two or three people who actually do. The bigger the database gets, the more this dynamic embeds itself. And it compounds.

“The best return on investment is to pull from the database because it costs less than going out in the field. But with time, that results in long lists of irrelevant matches.”

This is not a story about bad actors. It is about what happens when a business model optimises for scale and efficiency at the same time. The two start to work against each other, and the client notices before the firm does.

The Iq Edit
The Database Value Lifecycle
Value does not just plateau — it starts to decline. Here is why larger databases often hurt quality.
High Low Peak Value Year 0 Incentives shift Database bias sets in Relevance declines
Growth
Every expert added increases coverage and relevance
Plateau
Scale creates incentives to default to existing records
Decline
Long irrelevant match lists replace genuine sourcing
“A bigger database can actually result in the opposite effect.” — Omar Yamak, CEO, Infoquest
Infoquest

Going out in the field

Infoquest was built around the opposite approach. Every brief gets sourced from scratch. No filtering a static list. Instead, the team pulls from public databases, blog posts, research reports, LinkedIn, and an expanding set of AI search tools, then cold calls, has actual conversations, and vets people before a single profile gets sent to a client.

The output looks different, too. Where a large incumbent might send fifty names, Infoquest sends two to four. The idea is simple: a consultant or investor does not need a list. They need the right person, already checked, with a clear reason why they fit.

There is a talent implication here that Yamak is direct about. The skills required to do this are completely different from the skills required to run a database search. One is a process. The other is closer to what he calls an art, something you get better at over time, something that requires instinct as much as method.

Two Models. One Clear Difference.
The Iq Edit
Legacy
Database-First Model
Starting point
Filter an existing static database for matching profiles
Output volume
50+ profiles per brief — client filters themselves
Sourcing effort
Associate checks if expert exists in system — low friction, low insight
Incentive
Lowest cost = use what is already in the database
Scale effect
Bigger database leads to more irrelevant results over time
Access model
Pre-paid bundles, 15+ calls minimum, thousands per call
vs
Infoquest
Custom Sourcing Model
Starting point
Every brief sourced fresh from public data, AI search, cold outreach
Output volume
2–4 curated profiles, pre-vetted and matched to the exact knowledge gap
Sourcing effort
Cold calls, referrals, blogs, reports — human and AI working together
Incentive
Every match is earned — no shortcut through a legacy list
Scale effect
Custom sourcing skills compound — harder markets build stronger capability
Access model
Pay-as-you-go, no minimum, flexible for any organisation

What AI actually changes

Ask Yamak about AI and expert networks, and he does not hedge. “If you don’t think AI is going to take over 95% of the search, you’re delusional.” The matching algorithms, the research phase, and the initial shortlisting will all eventually be done faster and more accurately by machines than by people.

Infoquest is already building toward this. A vision where, eventually, clients query the AI directly to surface candidates, then have the Infoquest team manage everything that happens next.

“Give clients the AI to find the expert. But let us manage the people relationship — because we are very good at doing it.”

That people relationship is the part Yamak keeps coming back to. The emotional intelligence to read a conversation. The cultural nuance to know why an expert in a particular market might be hesitant, and how to address that. The ability to walk someone through an expert call for the first time when they have never heard of the concept before.

None of that gets automated. And the firms investing in it now are building something that will matter more, not less, as AI handles more of the search.

The Iq Edit · Future Model
AI Finds the Expert.
Humans Build the Relationship.
“Human in the loop with AI is always more powerful than AI acting alone.” — Omar Yamak, CEO, Infoquest
🤖
AI Layer
Search, matching and operational efficiency
~95% of search volume
Expert matching algorithms: takes a brief and shortlists profiles in minutes
Research automation: scans reports, articles and social data at scale
Operational tasks: expert bios, brief summaries, admin workflows
Client-facing search tools: let clients query directly and surface candidates
🤝
Human Layer
Relationships, trust and cultural intelligence
Irreplaceable edge
Emotional intelligence: reading a conversation, addressing expert hesitation
Cultural nuance: understanding how experts operate across regions
Expert onboarding: walking first-time participants through the process
Prompting and directing AI: training algorithms toward quality results
“It is no longer about who has the biggest database. It is about who knows how to manage AI — and who has the people skills to keep it all together.” — Omar Yamak
Infoquest

The edge nobody talks about

There is one more thing worth knowing about how Infoquest got here. The firm started in the Middle East. deliberately, as the first homegrown expert network built for a region that the global players had entered but never really understood.

What that origin forced was a kind of sourcing capability that most established firms never had to develop. The Middle East is an emerging and underrepresented market in this industry. Experts are harder to find, less likely to be on any database, and often need to be educated about what an expert call even is before they will agree to one. The Infoquest team had to figure all of that out from scratch.

When the firm started winning clients in Europe and then globally, those skills turned out to be its biggest advantage. Knowing how to find experts nobody else can find. Knowing how to speak to experts nobody else knows how to speak to. Today, over 50% of Infoquest’s demand comes from Europe, and one of the larger traditional networks recently reached out asking Infoquest to source experts in a geography they could not access themselves.

The student becoming the supplier is a pretty good sign that the model is working.

The question the industry is still avoiding

Expert networks are not going anywhere. The demand for real insight from people who have actually done the work, in the right market, at the right time, is growing, not shrinking. If anything, AI flooding every other research channel with noise makes genuine expertise more valuable.

What is changing is the measure of quality. The database number was always a proxy. It is just that enough clients are now experienced enough to know it. The question shifting from “how many experts do you have” to “can you actually find the one I need” is not coming. It is already here.

The firms still leading with the big number might want to update their homepage.

About Omar Yamak

Omar Yamak is the CEO and Co-Founder of Infoquest. Before building Infoquest, he helped grow the North American office of Dialectica from 10 to nearly 200 people. He is based in the Middle East and is focused on taking Infoquest global.

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