An Aivell reads every application that lands in your jobs mailbox, scores it against the role's must-haves and nice-to-haves, keeps a ranked shortlist with a short written reason per candidate, and acknowledges every applicant politely. Candidate data is processed on a box in your office — nothing is sent to any cloud service.
starts when: A candidate emails a CV to your jobs@ mailbox
Post a decent job ad and a hundred applications arrive in a week. Screening them is repetitive reading under deadline: the same job description held against a hundred differently formatted lives, most of them clearly not a fit, a few of them excellent and buried at position seventy. By the time the pile is read, the best candidate has accepted somewhere faster.
The obvious fix — cloud screening tools — has an ugly cost. Applicants trusted you with their life story, their address, sometimes their salary history. Piping that to a third-party platform is the kind of processing that keeps data-protection officers up at night; reading it on your own hardware is privacy by design, in the literal GDPR Article 25 sense.
The scoring rules are a paragraph of plain language you can read, question and change. That, plus a reason logged for every score, keeps the process explainable to a candidate, an auditor — or yourself, three months later.
No flowcharts, no code — a chore is just a message to your Aivell, in your own words. It shapes it into a solid, guarded procedure and follows it to the letter. This one:
watch jobs@ for new applications
read the cv and cover letter, whatever the format
score against the role in the subject line — must-haves first, nice-to-haves second
a must-have missing — licence, language, work permit? mark not-a-fit and say which one
add every candidate to the shortlist sheet with score and a two-line reason
scores above 8 — tell Nadia the same day, not in the weekly batch
send each applicant a polite acknowledgement in their own language
never write a rejection — the sheet ranks, people decide
Connected with single-click passkeys — no copied tokens, no OAuth hell.
Every tool can be limited to exactly what this chore needs. For this one, you might set:
Into your own shortlist sheet, and nowhere else. The reading and scoring happen on the box inside your network — no CV is uploaded to a screening service or an AI provider. When a hiring round ends and you delete the data, it is actually gone, because it only ever existed on your systems.
It cannot. A guardrail limits its outgoing mail to acknowledgements, so a rejection is a message it is physically unable to send. It ranks, explains its ranking in two lines per candidate, and leaves the decision to the humans reading the sheet.
Always — the reason is written next to the score, in plain words, for every applicant. If you disagree with a pattern ("stop discounting career gaps"), you change the chore and the next applications are scored your way.
The usual PDFs and Word files, but also exported profile pages, scans, and the occasional photographed paper CV — the vision model on the box reads those too.
Candidates and interviewers coordinated by a private AI colleague — slots offered, rooms booked, reminders sent — while applicant data stays behind your firewall.
Joiner and leaver checklists run themselves — IT notified, sessions booked, progress tracked — by an AI colleague that keeps HR data inside the office.
An Aivell is an on-premise AI colleague: a small box that plugs into your network, runs its own AI onboard and takes the repetitive work off your desk. Prompts, documents, data — nothing ever leaves your office. Unplug the internet: it keeps working.
You describe each task as a chore, in plain language. Your Aivell turns it into a solid, guarded procedure and runs it in the background — with guardrails on every tool, a complete audit trail, and approvals in your hand when you want them. One-time setup, fixed monthly fee. No tokens, no overages.
onboard inference · air-gapped · fixed monthly fee