Nexhunter reads your thousands of CVs, ranks them against every need, and runs the technical interview for you. You decide — faster, and on facts.
A file three people edit, nobody re-reads, and which never says who is available today.
| Need | Profile | Rate | Free? | Chased? | Status |
|---|---|---|---|---|---|
| Java Tech Lead | Karim B. | 620 | ? | — | waiting on hiring manager |
| Java Tech Lead | Salma T. | ? | yes | ? | to qualify |
| Data Engineer | — | — | — | — | lost — staffed elsewhere |
| Network DevOps | Yanis M. | 470 | on assignment | yes | to review |
| QA Automation | ? | ? | ? | — | ? |
| Project manager | M. Ait H. | 580 | ? | — | chase up? |
The CV arrives by email, gets retyped into the file, then copied again into the client folder.
A staffing request drags for three days, nobody knows on whose desk. The client already received three profiles from elsewhere.
Nothing to compare two profiles on the same scale. Bringing in an expert costs more than the placement’s margin.
Two candidates fit, one earns twice as much. Without rate and margin in sight, you choose blind.
Consultants billing zero between assignments, while a need that fits them goes to external sourcing.
Not promises — design choices, and what they spare you.
The candidate talks to an AI that really digs: it follows up on vague answers, asks for examples, has them write code. You get a reasoned report — not a score.
That is what makes two seniorities comparable on the same scale — without booking an €800-a-day expert to discover the profile doesn’t hold up.
The European regulation classifies recruitment AI as high-risk. Nexhunter is built for that requirement, not retrofitted afterwards.
Nexhunter : You say you set up Kafka on your last project. What made you choose it over a classic queue?
Candidate : We needed to replay events after incidents, and several consumers on the same stream.
Nexhunter : Write me a topic consumer with error recovery.
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@KafkaListener(topics = "orders") public void onOrder(ConsumerRecord<String, Order> rec) { try { service.handle(rec.value()); } catch (RetryableException e) { dlt.send(rec, e); // targeted retry }
Before, my source of truth was an Excel file and my inbox. I picked up CVs by hand on LinkedIn, retyped them, and found out about delays once the need was already lost.
Adil — founder, ITELIA · recruitment & payrolling firm
Three moments, seven instruments. A recruitment firm starts with Antenne and Vivier, an IT consultancy with Sonar and Vigie — the rest wait for volume to come.
Not performance promises: measuring instruments. In the first month they light up — and you can’t steer what a spreadsheet never tells you.
From need received to candidate validated, stage by stage. And above all: where it blocks, and with whom.
CVs turned into profiles, needs broken down, applications matched to the pool: what you no longer do by hand.
Target versus realized margin per placement, bench cost, average time before re-staffing.
Bring one assignment brief and three CVs. You get the ranking, the reasons, and the interview report before the call ends.
Get a demoThirty minutes · Answer within 48 h · Proposal built after the call