Automation

Agent Kit Hiring

A hiring automation system that evaluates candidates by analyzing resumes and providing objective scoring for roles and sends emails to the candidates.

Deploy AgentKit View Demo

Overview

A hiring automation system that evaluates candidates by analyzing resumes and providing objective scoring for roles and sends emails to the candidates.

Key Features

✅ AI-Powered Resume Analysis

Leverage advanced AI models to analyze resumes and extract key qualifications

✅ Job Category Management

Organize positions by department with detailed job descriptions and requirements

✅ Real-time Candidate Evaluation

Get instant scoring, strengths, weaknesses, and hiring recommendations

✅ Automated Scoring System

Objective 0-10 scoring based on job requirements and candidate qualifications

✅ PDF Resume Upload

Seamless resume upload with Vercel Blob storage integration

✅ Dark Mode Interface

Modern, professional UI with purple and cyan accent colors

Quick Start

Click "Deploy AgentKit" to begin

Configure integration settings

Test in sandbox environment

Deploy to production

Estimated setup time

~10 minutes

Resources

Documentation Contact Support

Vertical use cases

HR Teams

Automate initial candidate screening and reduce time-to-hire by 60%

Recruiters

Quickly evaluate multiple candidates with consistent, objective criteria

Hiring Managers

Get AI-powered recommendations with detailed strength/weakness analysis

Startups

Scale hiring operations without building large HR infrastructure

Remote Teams

Standardize candidate evaluation across distributed hiring teams

High-Volume Hiring

Process hundreds of applications efficiently during growth phases

FAQ

The system uses OpenAI's o3-mini reasoning model for deep analysis, providing consistent and objective evaluations based on job requirements. All recommendations include detailed explanations.

Yes, job categories and descriptions are fully customizable in lib/jobs-data.ts. You can add unlimited categories and jobs with custom requirements, locations, and types.

The system supports PDF resumes, which are uploaded to Vercel Blob storage and analyzed by the AI. The resume URL is securely passed to the Lamatic workflow.

Resume files are stored in Vercel Blob with secure URLs. Analysis results are processed in real-time and displayed to the user. No candidate data is permanently stored without your explicit implementation.

Yes, the system is built with Next.js and can be extended to integrate with any ATS via API. The modular architecture makes it easy to add custom integrations.

Costs depend on your Lamatic plan and AI model usage. The o3-mini model typically costs $0.005-0.01 per evaluation. Check the _meta field in responses for exact token costs.