From the first resume in the door to a signed offer. Matching, screening, scheduling, and reporting, built as one connected pipeline instead of a stack of disconnected tools.
Every application is read the way a good recruiter would read it, against what the role actually needs, and the reasoning is shown rather than hidden behind a number.
A 0 to 100 match score for every candidate, generated by reading the resume against the role’s real requirements. Not a keyword count.
Scores are shown relative to the actual applicant pool for that role, as in "top 8% of 340 applicants", rather than a raw number with nothing to compare it to.
Every AI read flags when a resume was too long to fully analyze, and discloses what share of its claims it could verify against the source document.
Built for a use case where a wrong AI claim has real consequences for a real person, so every claim is checked and every attempt to game the model is caught.
Every strength or weakness the AI cites is checked back against the actual resume text. Quotes it cannot substantiate are flagged, never presented as fact.
Resumes are screened for hidden text trying to manipulate the AI into inflating a score, before they’re ever analyzed.
Every AI call is logged with the model used, prompt version and timestamp, so any score can be traced back to exactly how it was produced.
Vague job requirements produce vague candidate matches, so JobSignal helps tighten the brief first.
A short guided intake generates a structured job brief from a hiring manager’s answers, ready to edit instead of a blank page.
Flags vague or conflicting requirements before the role goes live, with a market-reality check when expectations are out of step with what’s realistic to find.
Bring an existing pipeline of candidates in at once, without the manual data entry or the compliance gaps.
Drop dozens of CVs at once. Name, email and phone are extracted automatically, duplicates are caught, and anything ambiguous lands in a review queue.
Candidates who didn’t apply directly are automatically emailed a GDPR-compliant notice explaining their CV is on file and how to have it removed.
Hard requirements like a certification, a location or a clearance shouldn’t need a human to check every single application.
Configurable must-have questions are evaluated automatically against every application, with a manual override for when a recruiter’s judgment should win.
A Kanban view of every role’s candidates, with the structure and audit trail a real hiring process needs underneath it.
Applied → Screening → Interview → Offer → Hired out of the box, freely renamed, reordered, or extended per role.
A dashboard-level funnel shows how many candidates have reached each stage org-wide; every candidate carries a small progress indicator wherever they appear.
Interviewers submit individual, attributed feedback. Every response stays visible on its own, rolled up into a recommendation summary.
From proposing times to walking into the interview prepared, scheduling that runs itself.
A recruiter proposes up to three times and the candidate picks one with a single click. No account, no sign-up.
A Zoom meeting is created automatically the moment a time is confirmed, with a calendar invite sent to everyone involved.
Interviewers get an automatic email the morning of the interview with the CV attached and a condensed AI briefing, so nobody walks in cold.
After the interview, a three button email (Accepted, Rejected, On Hold) automatically advances the candidate’s pipeline stage.
A one-page candidate summary built to be shared, not just viewed in-app.
Score, band and the AI’s key findings condensed onto one page and emailed as a PDF, so you can brief a hiring panel who never logs into JobSignal.
Open roles, presented the way your company presents everything else.
A branded, public career site for every organization with your logo and your open roles, live in minutes. Candidates apply directly.
The numbers a hiring team is asked for in every leadership review, without a spreadsheet.
See how long roles actually take to fill and where candidates drop off stage by stage, pulled from data you are already generating.
Real organizations, real teams, real permissions.
Admin, Recruiter, Hiring Manager, Interviewer and Viewer roles, plus per-job hiring teams, so everyone sees what their role calls for.
Candidate data is personal data, handled that way throughout rather than bolted on afterward.
Each organization sets its own candidate-data retention window; deletion removes the full record and every associated file.
Never locked into one AI vendor. Configurable at the platform level.
Run candidate analysis on a self-hosted local model, where resume data never leaves your infrastructure, or point it at a commercial API.
Not roadmap promises. Every one of these is live in the product today.
Every AI claim is checked against the source resume and flagged if it cannot be verified. No confident black-box scores.
Actively detects resumes engineered to trick an AI scorer, a real attack surface most hiring tools ignore completely.
Calibrated against the real applicant pool for that role. "Top 8%", not an arbitrary 84/100 with nothing to compare it to.
Candidates confirm an interview time with one click. No account, no app, no back and forth email thread.
A self-hosted AI option means resume and candidate data never has to leave your own infrastructure.
An automatic morning-of briefing with the CV and key AI findings, so no interviewer shows up cold.
Free for life for organizations that register in 2026.