Highlights
Project snapshot
- Unifies role creation, screening, interviews, operations, and AI cost analytics in one hiring system.
- Spans multiple services, including production web apps, onboarding flows, Artemis AI backends, and LLM tracking.
- Pairs enterprise hiring workflows with dedicated cost and usage visibility for production AI operations.
Overview
A hiring platform built as an operating system
Navero is a multi-service AI-powered hiring platform designed to centralize the full hiring lifecycle, from role definition and candidate intake to interviews, operations, and AI cost visibility.
The product spans the production web platform, next-generation onboarding and public job flows, the Artemis AI backend, and a dedicated LLM usage tracking service so recruiters, operators, and leadership can work from a shared system.
Problem
Hiring teams were forced across disconnected workflows
- Hiring workflows were fragmented across separate tools for role setup, screening, interviews, and operations.
- Candidate screening was manual and inconsistent, slowing down decision-making and creating uneven evaluation quality.
- AI usage lacked clear cost and performance visibility, making model operations hard to manage at scale.
Solution
Navero combines product workflows, operations, and AI infrastructure
- Built Navero as a hiring operating system with dedicated services for product workflows, AI evaluation, and LLM cost tracking.
- Structured the platform around modular screening pipelines, candidate intelligence, interview orchestration, and internal operations.
- Added separate tracking for model usage and billing so AI performance and spend are treated as first-class product concerns.
Core Features
Platform capabilities across the hiring lifecycle
AI Hiring Intelligence
- AI-generated job descriptions
- Skill extraction and company research
- Role pack generation
Screening System
- Knock-out questions (KOQs)
- CV parsing and scoring
- MCQ tests plus voice/text smart screening
Candidate Management
- Trust scores and candidate insights
- Recruiter dashboards
- Pipeline management
Outbound and Interviews
- CSV/PDF candidate imports
- Outreach templates and sourcing tools
- Scheduling, reminders, interview loops, and prep kits
Operations and Billing
- Stripe subscriptions
- Admin dashboards and customer success tooling
- LLM usage and cost analytics
Architecture
Multi-service architecture with AI tracking as a core layer
Users
→ Frontend (Next.js apps)
→ API routes
→ PostgreSQL / Supabase
→ Artemis AI (FastAPI)
→ LLM providers
→ LLM tracking service
- Split across navero-web, navero-prototype-main, navero-llm, and navero-llm-tracking.
- Uses a dedicated AI service layer rather than embedding all orchestration directly into the product UI.
Technical Highlights
Production depth across application and AI infrastructure
- Built with 104 Prisma models and 209 migrations.
- Supports multi-provider orchestration across OpenAI, Anthropic, Gemini, Grok, and LangChain-powered flows.
- Combines business operations, product workflows, and AI analytics inside the same platform architecture.
Business Value
Operational gains from a unified hiring stack
- Reduces hiring tool fragmentation by centralizing product, operations, and AI workflows.
- Improves hiring consistency through structured evaluation pipelines and shared candidate intelligence.
- Creates transparent AI cost management for teams running production hiring workflows.