By dropping the AI interview in favor of the channel candidates already live in — and choosing Linq as the iMessage infrastructure behind it — Clera built a talent agent that represents 172,686 candidates and is aiming for a million.
Clera is an AI talent agent. It represents the candidate, not the company. A person tells Clera what they want, what they've done, and what their dealbreakers are, and Clera goes out and gets them introduced to startups that fit. Candidates don't pay. Companies pay when they hire.
The company was founded by Sebastian Scott, Alexander Farr, and Daniel Wintermeyer, three founders who met in Germany and moved to Berlin to work in the startup ecosystem there. Alexander had grown an AI-messaging startup to $10M ARR and run recruiting and GTM engineering inside it. Daniel had built a marketplace processing more than $30M in volume and led fifteen engineers at an HR scale-up. All three had been on both sides of a hiring process they thought was broken.
"One of our core values is that we want to be talent-first. We want to make sure that talent has the best experience with us." — Alexander Farr, Co-founder
With Linq, Clera found the infrastructure to deliver that experience in the one channel their candidates actually answer.
Three founders who had lived the problem from both sides
Before Clera, Alexander was chief of staff at Superchat, a German startup built on the WhatsApp Business API, where he did a large amount of internal recruiting for up-and-coming AI tools. The work made the gap obvious. Recruiting was not really solving the problem of connecting candidates and companies for the right fit.
Daniel came at it from the engineering side. Leading a team of up to fifteen engineers meant he spent years thinking about what one player on a team can do for every project a company is running.
"I was working with a lot of headhunters as an employer, as a hiring manager, as well as later on as a candidate as well." — Daniel Wintermeyer, Co-founder
The thesis that came out of it was bigger than recruiting.
"The biggest problems consumers have are finding a job, finding someone to date, and finding housing. Those are the biggest opportunities for marketplaces, but also the biggest problems in everyone's lives — and really unsolved."
Recruiting is a market measured in the [TK: $1T? — audio unclear] and a graveyard of failed companies. What made it worth another attempt was AI. The team saw a paradigm shift: for the first time, candidates and companies could be connected at scale and with real intelligence about fit.
Starting with AI interviews, and learning why they don't work
Like many players in the space, Clera started with AI interviewing. The promise was easy. A candidate talks to the AI for fifteen minutes, maybe half an hour. The AI gets deep context, understands what the person is working with and what they want, and places them into the right job.
It did not survive contact with candidates.
"We've seen quite quickly that this is deterring a lot of talent completely."
The interview was a gate, and the best people would not walk through it.
Meeting talent in the channel they already use
So Clera pivoted. Instead of pulling candidates into a product, they went to the candidate's channel of choice: email, WhatsApp, and most importantly, iMessage.
That choice follows directly from being talent-first. A blue bubble is where people already talk to the ten people closest to them. It is the difference between a candidate reading a message and a candidate ignoring one. For a product whose entire value is getting a person to the right conversation, the channel is not a delivery detail. It is the product.
Choosing the infrastructure behind the blue bubble
[TK — Linq evaluation section. To match Poke and Tomo, this needs: what Clera's criteria were (deliverability, reliability, security/SOC 2, scale, price), who else they looked at, and why Linq won. One quote from Alexander or Daniel here.]
[TK — Implementation. How fast Clera got their first line live on Linq, what the integration lift was, and one example of Linq shipping something in response to a Clera request.]
A company built out of a Lima Airbnb
Clera started in Berlin, which meant working past midnight to overlap with the US. The fix was blunt: fly somewhere on a US timezone. They picked Lima for convenience and curiosity, booked no return flights, and came home eight months later for Christmas.
The two weeks in the cheapest Airbnb in Lima became four, then six. When Peru got cold, they moved on. The eight-month trip became a year-long detour through Latin America. In Mexico, the house had unstable Wi-Fi, no running water, and intermittent electricity — "electricity was not the worst part, to be honest." The routine was nine in the morning, into an Uber, and eight people taking over a café for the day.
They were hiring the whole time, with a pitch that filtered hard.
"Do you want to work 24/7 with us? Do you want to live with us? And especially, do you want to relocate to Latin America?"
It worked. They found remarkable people that way.
172,686 candidates, and a target of a million
When the founders recorded this, the number was six figures and climbing.
"We just hit 100,000 candidates that have been using us over the last year."
Today Clera represents 172,686 people. The ambition is another order of magnitude past that.
"We want to hit a million candidates within the next year that are benefiting from using us."
What's next: access is the product
Clera's founders think work is heading somewhere structurally different, where a job is more optional than it is today. They are direct about not knowing all the implications yet. What they are confident about is the transition.
People need more support to find the best opportunities during that shift, and they need a way to become ready for it. In practice, that comes down to access.
"There are thousands of applications going into the normal process, and people are just looking for a way to get alpha — to get access to actually talking to the right founders. And likewise, on the company side, getting access to the best candidates."
That is the marketplace. Underneath it is the intelligence: understanding what the candidate is looking for, understanding what the company is looking for, and then the only question that matters.
"And then: can we make those intros?"
