aicvscreening's answer

• Startup founders hiring quickly
• Recruiters handling high volumes of applicants
• Small and mid-sized teams without complex ATS systems
• Hiring managers who want to save time on initial screening

Anyone who needs to review many CVs efficiently.

aicvscreening's answer

• Early-stage startups
• Indie founders
• Small recruiting agencies
• Tech hiring managers

aicvscreening's answer

AI CV Screening focuses on speed, clarity, and actionable insights, not just keyword matching.

Instead of simply filtering resumes, it: - ranks candidates by real job fit - explains why each candidate matches or doesn’t - highlights pros & cons for quick decision-making - works with multiple file formats and bulk uploads (.ZIP)

It’s built to replace manual CV review, not just assist it.

aicvscreening's answer

Most tools are complex ATS platforms or basic keyword filters.

AI CV Screening is different because it: - requires no setup or integrations - works instantly with uploaded CVs - provides transparent scoring (not a black box) - is optimized for fast decision-making (100 → top candidates in minutes) - offers a generous free tier (50 CVs)

It’s ideal for teams that want results quickly without heavy systems.

aicvscreening's answer

AI CV Screening was built out of frustration with manual resume review.

Reviewing dozens (or hundreds) of CVs is repetitive, time-consuming, and often inconsistent. Even with existing tools, the process still required too much manual effort.

The goal was simple: turn CV screening into a fast, structured, and automated process

Instead of reading every resume, users can focus only on the best candidates.

aicvscreening's answer

AI CV Screening is built on a modern, AI-ready SaaS stack (based on GoLiveKit), focused on speed, scalability, and type safety.

Core technologies: - Next.js + React - full-stack framework with server-side rendering and modern UI architecture - Node.js - backend runtime for server logic - TypeScript - end-to-end type safety

Database & backend: - PostgreSQL - relational database - Prisma ORM - type-safe database access

AI layer: - AI SDK (LLM APIs) - for CV analysis, scoring, and explanations

API & data handling: - oRPC - type-safe API layer - React Query (TanStack Query) - data fetching and caching

Frontend & UI: Tailwind CSS + shadcn/ui - styling and component system - Radix UI - accessible UI primitives

Validation & quality: - Zod - schema validation - Biome - linting and formatting - Vitest - testing

Authentication & infrastructure: - Better Auth - authentication and session management - Cloud storage (S3-compatible, e.g. R2) for file uploads

This stack enables fast development, strong type safety, and seamless integration of AI features.