Nova Recruiter
Searches 800M+ public profiles in plain English, ranks candidates by actual track record instead of keyword matches, and sends the outreach messages for you.
🔗 Visit Nova Recruiter
Description
Sourcing candidates the traditional way means typing boolean keyword strings into a search box and hoping the right person has the right buzzword on their profile — then manually messaging dozens of people and following up yourself. Nova Recruiter replaces that workflow: you describe who you're looking for in plain English, it ranks candidates by what they've actually done rather than keyword overlap, and it sends and follows up on the outreach across multiple channels automatically.
Nova Recruiter is an agentic AI sourcing platform searching 800M+ public profiles, using what it calls merit-based ranking — evaluating a candidate's track record and achievements instead of matching keywords — surfaced through a natural-language search interface. Outreach runs as multichannel campaigns (email, LinkedIn, and Nova's own platform) with automated follow-ups, and the company reports 2.5x higher reply rates and roughly 98% time savings versus manual sourcing (about 20+ hours saved per vacancy). It integrates with 60+ ATS platforms and exposes itself through the Model Context Protocol, meaning it can be driven directly from Claude, ChatGPT, Gemini or other AI assistants rather than only its own web UI. Pricing is credit-based (1 credit = 1 candidate contacted, regardless of message volume): Free (EUR 0/month, 10 credits, limited search), Starter (EUR 49/month, 50 credits), Growth (EUR 199/month, 300 credits), and custom Enterprise.
💬 Our review
The short version: if sourcing candidates manually is eating a full day per open role, Nova Recruiter's free 10 credits are enough to see whether merit-based ranking actually surfaces better candidates than your usual boolean search before you pay anything.
Its real differentiator against a classic sourcing tool is twofold: the merit-based ranking (evaluating what candidates have actually accomplished, not keyword density) and native MCP support, which lets you drive sourcing from inside Claude or ChatGPT instead of learning another dashboard — a genuinely forward-looking integration choice most recruiting tools don't offer yet. The reported 2.5x reply rate and 98% time savings are vendor numbers, worth verifying against your own vacancy rather than taking at face value. Credit pricing (1 credit per candidate contacted, not per message) is fair and predictable, and Growth at EUR 199/month for 300 candidates works out to roughly EUR 0.66 per contacted candidate — reasonable next to a recruiter's hourly cost. Best fit for in-house recruiters and headhunters doing regular outbound sourcing; less useful if your hiring is inbound-only or you already have a strong existing ATS-native sourcing workflow.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Gratuit (0 €/mois, 10 crédits, recherche limitée) ; Starter 49 €/mois (50 crédits) ; Growth 199 €/mois (300 crédits) ; Enterprise sur devis. 1 crédit = 1 candidat contacté
Pros
Classement des candidats par mérite (parcours réel) plutôt que par mots-clés
Recherche en langage naturel sur 800M+ profils publics
Support natif du Model Context Protocol — pilotable depuis Claude, ChatGPT, Gemini
Intégration avec 60+ plateformes ATS existantes
Cons
Chiffres de performance (2,5x taux de réponse, -98% de temps) fournis par l'éditeur, à vérifier sur son propre cas
Modèle par crédits qui limite le volume de contacts sur les formules basses (10 gratuits, 50 en Starter)
Peu utile si le recrutement est purement entrant (inbound) sans besoin de sourcing actif
Tarification en euros, moins immédiatement comparable pour les équipes en dollars