Keyword search misses good people. Talhue reads every resume the way a recruiter does, so you can ask in plain English and get a ranked shortlist with the reasons behind each match.
Read as: title data scientist · skills (python AND pandas) OR scikit-learn
Most applicant tracking systems search with keywords and Boolean strings. The right candidate is often in your database, and the search never shows them.
Different titles for the same job. Search for "software engineer" and you lose the "Sr. Backend Developer" and the "SDE II".
Old experience looks new. A skill used ten years ago counts the same as one used last month.
Keyword stuffing wins. A resume with a long skills list outranks one that shows real work with those skills.
Boolean strings are hard to get right. One misplaced parenthesis changes the results, and nobody can tell.
Candidates flow in automatically. Talhue reads each resume once: job history, years of experience, role family, industry, and AI experience.
"ICU nurse in Austin with CCRN" or "SRE with Kubernetes and Terraform or Pulumi". Talhue keeps your and/or logic exactly as you wrote it.
Every candidate shows which requirements they meet, which are close, and which are missing, plus whether the experience is current.
Each requirement is checked against each resume, so a match depends on what the resume says, not whether the exact word appears.
See at a glance whether a candidate does this work in their current role, did it in their last role, or only years ago. Sort and filter by it.
Know who uses AI tools, who has built AI into their team's work, and who leads AI adoption, in any profession.
Software, nursing, physicians, sales, skilled trades, data centers and more. Add your own role families and role-specific checks.
Every past employer is tagged with its industry, so a candidate's healthcare or fintech background is on record.
Industry filter: coming nextRun on OpenAI, Anthropic or Google, set per client. Each client's data goes only to the provider it chose.
Talhue matches candidates with TypeSafe System One and its Jev model. Jev answers questions about a resume with a yes or a no and a confidence. It doesn't write text, so it can't invent a skill or a job the candidate doesn't have.
Checking a requirement is a quick yes-or-no answer, not a written opinion, so Talhue can check every requirement against every resume in a search.
Short answers cost far less than generated text. Searching your whole database stays affordable, and each search reports what it cost.
Every answer is a yes, a no, or a choice from a fixed list. Recruiters get a match they can check against the resume, not a summary that might be wrong.
Talhue is open source under the Apache 2.0 license. Your team can run it on your own cloud. Or we connect it to your ATS, tune it to your roles, and run a pilot on your real candidates.
The fastest way to judge search is on a real open role. We'll connect Talhue to your ATS and run your current searches side by side with your existing tools.
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