Recruiting likes talking about “intuition”.

But intuition is often just a polite word for a lack of data. It’s the comfortable blanket that covers up hiring processes that are slow, inconsistent, and, more often than we’d like to admit, biased. In a market that moves faster than a Slack notification, the companies that continue to hire based on gut feeling and "vibes" are essentially gambling with one their most expensive assets.

In the latest episode of The Ramp, we sat down with Fredrik Schjoldager and Jonas Tjomsland, two of the co-founders of Soolv, to discuss how they are using AI to dismantle the most manual, soul-crushing parts of the recruitment process.

The takeaway was clear: the screening process needs to change. The winners in 2026 won't be the companies that throw more human hours at a pile of CVs; they’ll be the ones with the systems to identify top talent instantly, leaving the "human in the loop" to do what humans do best: sell the vision and assess cultural fit.

Moving past the "luxury problem"

Top-tier employers, the law firms, management consultancies, and investment banks, have what Fredrik calls a "luxury problem": they are flooded with high-quality applications. But when you have 1,000 "perfect" candidates for 10 roles, that luxury quickly turns into a logistical nightmare.

Today, companies solve this by mobilizing huge teams of expensive consultants and lawyers to spend countless hours manual-screening documents. It’s an inefficient use of brainpower that leads to "shortcuts", only looking at specific universities or set GPA cut-offs.

Soolv’s approach is to reduce that manual work. By using AI to interpret and structure unstructured data (CVs, transcripts, cover letters), they turn a PDF into a searchable, objective data point. This isn't about making the decision; it’s about making the information required to make that decision accessible in seconds, not weeks.

Surfacing the hidden outlier

The traditional screening process is inherently biased toward the "known". If a recruiter is pressed for time, they will naturally favor a candidate from a familiar school or a recognizable previous employer.

Jonas experienced this firsthand. Despite graduating from a prestigious institution like Cambridge, he found the transition back to the Norwegian job market surprisingly difficult because he didn't fit the local "NTNU/Indøk" mold that many recruiters were conditioned to look for.

By extracting objective, undeniable facts, like a specific grade in a high-level math course or a particular trait evidenced across multiple documents, AI can surface the "untraditional" profile. It levels the playing field, ensuring that the candidate who went to an unknown school in Europe but crushed every technical subject is seen alongside the Ivy League graduate.

Building the "learning loop"

One of the most provocative ideas from the conversation was the concept of the recruitment "learning loop". Most companies hire, and then they forget. They don't systematically track which traits in an initial application actually correlate with high performance two years later.

Because Soolv turns applications into structured data, it allows companies to find patterns. If the data shows that high performers in your specific sales team all shared a certain background trait that wasn't previously on your radar, you can adjust your search in real-time. Recruitment moves from being a series of isolated events to a continuous, data-driven system of improvement.

The death of the generic prompt

There is a lot of "AI hype" in HR Tech right now, with every ATS claiming to have a magic "AI button". But as Jonas points out, a generic "one-shot" prompt like "Is this candidate motivated?" is useless. It’s subjective, unreliable, and often hallucinations-prone.

The future isn't a generic chatbot. It’s a specific, instruction-heavy engine designed to do one thing. Extract objective data with 100% accuracy. The human recruiter shouldn't be asking the AI what it "thinks"; they should be using the AI to build a perfect, filtered view of the talent pool so they can start the real work of interviewing.

From inputs to answers

Looking further ahead, the shift in HR Tech is moving from asking for inputs to providing answers. We are entering an era where the "human in the loop" is a strategic architect rather than a data entry clerk. As the market matures, the systems that win won't be the closed, "all-in-one" platforms, but the intelligent layers that can integrate across the entire ecosystem. Whether it’s screening or internal knowledge management, the goal is the same: providing the decision-maker with the right data at the right time. The question isn't whether AI will change your hiring process, it’s whether you’ll use it to find the outliers your competitors are currently ignoring.