How Judgment Labs Searches for Evidence of Excellence.

Wrangle helps Judgment find exceptional candidates whose value cannot be captured by job titles, keywords, or standard recruiting filters.

“We’ve switched from using different platforms for sourcing, enrichment, outbound, and inbox to doing it all on Wrangle.”

After raising $32 million to give agents better judgment, Judgment Labs is focused on building an exceptional team.

In the modern hiring environment, startups compete for top technical talent with large tech companies and frontier AI labs. Those organizations have established brands, large recruiting teams, and the ability to offer candidates greater stability. Judgment has a different value prop: early ownership, rapid personal growth, and the opportunity to build important systems from an earlier stage.

Henry Xiao, Head of Talent at Judgment Labs, is direct with candidates about that tradeoff. Judgment is looking for the people who want the early ownership, variance, responsibility, and potential upside of joining an ambitious early-stage team.

That makes candidate identification unusually nuanced.

A candidate’s strongest signal might not appear in their LinkedIn profile. Rather, it may be found in engineering work, research, mathematics, physics, competitive achievements, or the speed at which they have taken on increasingly difficult problems.

These are problems that keyword search cannot solve.

“We do a lot of searches that go beyond keywords. We really look for people who spike in excellence in one way or another.”

Wrangle gives Henry a way to search for those qualities directly.

Most recruiting systems begin with a fixed data model. The recruiter can filter by title, company, location, seniority, education, and years of experience. Boolean search makes the query more complicated, but still doesn’t allow the recruiter to introduce a fundamentally new criterion, or get sufficiently granular in a search.

This is also very common in existing “AI search” tools. Underneath the chat box, the system still translates the request into the same preset Boolean filters.

Wrangle is built around natural-language evaluation. Henry can write the hiring standard in ordinary language, including criteria that have no equivalent dropdown or database field. Wrangle then evaluates profiles according to that standard.

“You don’t have confined preset filters. You’re able to define new evals in natural language, and Wrangle is able to filter profiles through them.”

This lets Henry search in Wrangle exactly how he’d talk to a sourcer.

For example, Henry might want to find people working on agents at Stripe. Boolean/filter search would index on adjacent keywords or titles, but the result depends on whether each profile contains the expected terminology. Wrangle can assess whether the person’s experience is substantively relevant, even when the work is described differently.

That is one reason Henry now relies on Wrangle instead of LinkedIn Recruiter or another tool.

“If I want to look for people who work on agents at Stripe, I’m a lot better off doing that on Wrangle than LinkedIn.”

As Henry reviews candidates, he can improve the search in place. He can rewrite the evaluation, adjust the criteria, and have Wrangle update the same candidate pool. He does not need to discard the search and reconstruct it from the beginning.

“Now I’m able to update the eval, update the filters, and adjust it in real time rather than having to start a brand-new query.”

Once Judgment identifies the right candidates, the workflow can continue in Wrangle. Henry can enrich contact information, organize profiles into collections, run outbound sequences, distribute sending across accounts, and review campaign analytics. The original search context remains attached to the candidate instead of being lost across exports and disconnected systems.

Henry sees that continuity as one of Wrangle’s most important advantages.

“Once everything really goes all in one, this is truly unlocked. You don’t have the context leakage. It can be more valuable than all of the tools combined.”

Wrangle carries the company’s hiring thesis through the entire workflow. The criteria used to discover a candidate can inform evaluation, organization, and outreach; when there is one surface to do all of these things, context isn’t lost due to data migration across tools.

Judgment remains deliberate about growth.

“The worst thing you can do is hire maybes. We really want to keep the team talent density extremely high.”

Wrangle helps Judgment expand the search without compromising that philosophy. It gives Henry a way to find unconventional candidates, evaluate them against a precise company-specific standard, and engage them from the same system.

“If you’re on the fence about trying Wrangle, just do it.”