You submitted the brief. The network sent you three profiles within 24 hours. On paper, they look fine. Former VP at a relevant company. Ten years of experience. Sector match checks out.
Then you get on the call. Five minutes in, you realize this person has not been close to the problem you are studying in three years. Their knowledge is stale. Their perspective is generic. You have just burned a call slot and an hour of your team's time.
Sound familiar? If you have used a large expert network, it probably does. And the reason is simpler than you think.
The Database Problem
Most large expert networks operate on a database model. They have hundreds of thousands of profiles, sometimes millions. When your brief comes in, their system runs a keyword search. Name, title, company, sector. The algorithm surfaces the profiles that match the most keywords. A recruiter (often junior) reviews the top results, picks a few that look reasonable, and sends them your way.
This is fast. It is also shallow.
The problem is not that these experts are unqualified. Most have genuine experience. The problem is that keyword matching cannot evaluate fit. It cannot tell you whether this former VP was in the division that is relevant to your question. It cannot tell you whether their experience is from the right time period. It cannot tell you whether they have the kind of operational depth that produces useful insight, or whether they are the kind of person who gives polished but empty answers.
Why You Keep Seeing the Same Names
Large networks have a recycling problem. A small percentage of their expert base is responsible for a large percentage of their call volume. These are experts who are available, responsive, and easy to schedule. They become the default.
From the network's perspective, this makes economic sense. Why spend time sourcing a new expert when you already have someone who picks up the phone? But from your perspective, it means you are getting the most available person, not the most qualified one.
This is especially problematic for niche or complex searches. The more specific your question, the less likely it is that the network's high-rotation experts will be a genuine fit. But because the incentive structure rewards speed and fill rate over precision, the network sends them anyway.
What Precision Sourcing Looks Like
Precision sourcing starts from the opposite end. Instead of searching a database for keyword matches, it starts with your question and works backward to the person who can answer it.
This means understanding the question deeply before proposing any experts. What decision is this research informing? What level of operational detail do you need? What time period is relevant? What specific dynamics are you trying to understand?
Then, instead of relying solely on a pre-built roster, a precision sourcing approach combines deep network relationships with targeted outreach. The network contacts people it has worked with before who fit the question. It also reaches into new territory, identifying and vetting people who may not be in any database but who have exactly the experience your question demands.
This takes more work. It takes experienced researchers who know how to evaluate fit, not just match keywords. It takes a team that is willing to tell you honestly if they cannot find the right person, rather than sending you someone who is close enough.
The Real Cost of "Close Enough"
When you are running a commercial due diligence on a compressed timeline, every call slot matters. A call with the wrong expert does not just waste an hour. It can introduce noise into your research, create false confidence in a thesis you have not stress-tested, or simply eat into a timeline that has no slack.
For a PE associate preparing an IC memo, the difference between a sharp expert call and a mediocre one is the difference between conviction and doubt. For a strategy consultant presenting to a client, it is the difference between looking sharp and having to qualify your findings.
The cost of "close enough" is not the call fee. It is the decision that gets made without the right intelligence behind it.
What to Look For
If you are evaluating expert networks, ask these questions:
First, how do they source experts? If the answer is purely database-driven, you are likely to get recycled profiles on complex searches.
Second, who is evaluating fit? If a junior recruiter is the only person between your brief and the expert profile, the quality control is minimal.
Third, will they tell you when they cannot fill a search? A network that always says yes is a network that prioritizes fill rate over quality.
Fourth, do they stay in the engagement? A network that hands you off after scheduling is a network that is not accountable for the outcome.
The expert network industry has scaled rapidly. That scale has created enormous value for clients who need high-volume, broad-sector coverage. But for the searches that actually matter, for the questions where the wrong answer has consequences, precision beats volume every time.
That is why we built TCE. Not to replace the big networks, but to be the one you call when their standard playbook is not enough.