What eighty roles at once teaches you about quality
For over a decade, Mark worked senior in-house recruiting roles at large multinational corporates, the kind of environment where a single talent partner might be expected to run dozens of open roles at once. That volume is exactly what taught him what quality costs when a recruiter is spread too thin.
The less you work on, the higher the quality you have.
He also learned, the harder way, that the standard playbook of posting a role and waiting rarely reaches the strongest candidates, because they are usually not the ones scrolling job boards. Winning them means going and finding them yourself.
The talent isn't always looking at adverts. You have to go away and win that talent.
Curious about the tool, then sold on the fit
Mark didn't come to TechTree chasing a bigger commission. He signed up curious about how the AI-driven matching actually worked, and just as interested in whether he could build a workable process around it that fit alongside everything else he runs.
I was curious as to how the AI connector works in terms of winning talent... the second bit for me was building the process of delivery, how I'm going to work within this, combine it with my day to day, and then deliver the talent.
What made him stay wasn't a feature. It was that the work bent around a week already full running his e-commerce business, instead of demanding a slot of its own.
I literally just schedule the calls around my calendar, usually midday, afternoon.
Closing the gaps and selling the roles
That instinct for quality over quantity is easiest to see up close. On one recent screening call, Mark went in knowing that a strong technical fit wouldn't land the placement on its own, and that the brief itself had a gap that could have caused problems either way. He didn't skip it or guess: he went off script and asked the technical follow-up questions himself, on things like container orchestration and vector databases, to confirm the fit was actually real before going any further.
Once the fit was confirmed and the expectations were set honestly, he moved into selling the specific role in front of the candidate, not a generic pitch, and kept a second live opportunity in reserve in case the first one didn't land, so the candidate had somewhere else to go rather than walking away from TechTree altogether. The result: the candidate went into the next stage already sold, with the rapport built in that one call doing work a generic screening note never could.
Using AI for the data, keeping himself for the close
Asked where AI actually helps, Mark points straight at the mechanical parts of the job: reading a CV against a job description to confirm the caliber and the fit, and turning a call into a clean written record for the hiring manager once it's done.
Where he won't let AI in is the interview itself, because he's watched what happens to trust the moment a recruiter disappears behind a keyboard.
You can lose trust with AI. You're just tapping on the keyboard, you're not really interested.
His read on the job hasn't changed since before AI existed. It was always sales wearing a screening badge, and closing the candidate is the part that actually earns the fee.
Recruitment, in my opinion, is: you help people get a job, but it's sales. You need to close that candidate on the role.
Control the input, and you control the quality
Asked what he'd tell a recruiter thinking about joining TechTree, Mark goes straight to the thing he thinks matters most: don't chase volume.
Less is more... it's going to be finite candidates. But if you get the right candidate, that startup is going to want that candidate.
The corollary, offered as his own working style rather than a rule, is that controlling what goes in is what controls the quality of what comes out.
If you control the input, you can control the quality of the process as well.