How do you screen every applicant against the same rubric fairly?
A hiring AI Employee connected to your applications inbox, the role's written rubric, and your calendar. It scores each resume against the rubric with supporting evidence and proposes interview slots for strong matches, and it never rejects anyone.
×Every open role brings in more applications than anyone can read carefully, so the first read gets rushed: a few seconds per resume, inconsistent from one reviewer to the next, and easy to drift from the criteria the role was actually written against. The same resume gets a different verdict from a different reviewer, or against a criterion the role never listed. Good candidates get skimmed past and the bar moves depending on who is reading. The usual fixes trade one problem for another. Keyword filters reject on the wrong signal and quietly drop good people. A rushed human pass is inconsistent by the afternoon. And an AI screener that scores against its own idea of 'good' is a fairness problem: opaque, unaccountable, and impossible to check.
Each inbound application triggers an AI Employee. The application starts an isolated run inside your infrastructure with the role's written rubric and scoped access to your calendar. It reads the resume against the rubric and writes a structured screen: strengths, gaps, a score, and supporting quotes as evidence. For strong matches it proposes interview slots on the hiring manager's calendar. A person decides every case.
See exactly how the work gets done.
Triggers on every application
The applications inbox, or the ATS, is the trigger. Each new application starts a fresh, isolated run seeded with that resume. One application, one run. Screens are independent and the pipeline processes in parallel.
Carries the role's rubric
The role's written rubric travels with the AI Employee as skills and memory: the required and preferred criteria, what strong evidence looks like for each, and how to score. It scores against this rubric and nothing else. When the rubric changes, the next application is screened against the new version.
Connects the resume and the calendar, with permissions you set
It reads the full application so the screen quotes what the candidate actually wrote, scores against the rubric with each strength, gap, and score tied to a supporting quote, and proposes interview slots on the hiring manager's calendar for strong matches. Credentials stay in memory, never written to disk, never exposed to the model.
Never rejects; a person decides
The AI Employee scores only against the written rubric and always surfaces the evidence behind every strength, gap, and score, so a decision can be checked. It never auto rejects. A person makes every advance or reject call. It produces the screen and the proposed slots; the hiring manager decides.
Arrives already screened
An inbound application arrives already read against the rubric: a structured screen with a score and the quotes behind it, and for strong matches a set of proposed interview times. The hiring manager reviews the evidence, decides, and confirms a slot.
Runs in your environment
Every application runs in isolation inside your own infrastructure; your data never leaves it. The run reads only the resume it's seeded with and reaches only the calendar; only the screen and proposed slots leave.
Permissions you set
The inbox, ATS, and calendar credentials stay in memory, injected at run time, never written to disk, never exposed to the model or the logs.
A named human makes every decision
The AI Employee never auto rejects. It scores only against the written rubric and always surfaces the evidence behind every score, and a person makes every advance or reject call.
Scored against the rubric, with evidence
Every score ties to a supporting quote from the application, so a screen can be checked against what the candidate actually wrote rather than an opaque idea of 'good'.
You own it
The rubric, the AI Employee's configuration, and its permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
The first read is now consistent and checkable: every application is scored against the same written rubric with the quotes that support each score, and strong matches arrive with interview times already proposed. The hiring manager spends their time deciding on evidence rather than skimming resumes, and every advance or reject call stays with a person.
Every application
Read against the same written rubric, with evidence
A person decides
No candidate is ever auto rejected by the AI Employee
3 systems
Applications inbox, rubric, and calendar in one AI Employee
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do you answer HR policy questions without pulling someone off their work?
Reads the HR inbox every 15 minutes, drafts replies grounded in documented policy, and escalates anything legal, personal, or comp related to Slack. It never sends and never makes a policy call.
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How do you schedule a whole interview loop without the back and forth?
Finds candidates ready to schedule, checks the panel's real Calendar availability, and drafts a slot proposal plus an invite per interviewer. It never sends and never touches a hiring decision.
×How do AI Employees source and draft candidate outreach every day?
Sources matching candidates for each open role, dedupes them against your ATS pipeline, and drafts a personalized outreach email per new match. It sources and drafts only, and nothing sends without the recruiter.
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