- Geography: India
- Industry: Financial Services
- Employees: 100+
- Solution: AWS
- Services: AWS GenAI Implementation
The client
Jarvis Invest is a mid-to-large enterprise with a high demand for continuous hiring across technical and business roles. Managing large volumes of candidate applications while maintaining hiring quality, speed, and consistency had become an increasingly complex challenge for the organization.
Client requirements
Standardized & Intelligent Hiring Process
Establish a consistent approach for creating job descriptions and accurately matching candidates to role requirements.
Automated Candidate Screening & Coordination
Reduce manual effort by automating resume screening, shortlisting, and interview scheduling across HR and technical teams.
Centralized Recruitment Visibility
Enable a unified platform to track candidate progress, interview feedback, and hiring metrics in real time.
Scalable & Secure Talent Acquisition
Strengthen recruitment operations with secure access, real-time monitoring, and a scalable hiring framework to support business growth.
Our approach
enreap designed and implemented a GenAI-powered recruitment solution on AWS for Jarvis Invest, built to automate the hiring lifecycle end-to-end and bring intelligent decision-making into every stage of recruitment. The solution centers on Amazon Bedrock for AI-driven content generation and candidate evaluation, and Amazon Kendra for contextual resume analysis, orchestrated through Amazon EC2 as the central application layer. Supporting services, including Amazon S3, Amazon DynamoDB, Amazon Cognito, and Amazon SNS, ensure secure, scalable, and efficient data handling and communication, while Amazon CloudWatch provides continuous monitoring and observability across the platform. As a result, Jarvis Invest achieved a significant reduction in manual effort and hiring turnaround time, improved candidate quality through AI-driven insights, and established a standardized, scalable, and data-driven recruitment process.
Our solution
Automated Job Description Generation
Recruiters submit job requirements through the application, and Amazon Bedrock generates optimized job descriptions with defined skills and responsibilities, stored in Amazon S3 with automatic recruiter notifications via Amazon SNS.
AI-Powered Resume Screening & Shortlisting
Amazon Kendra extracts skills, experience, and contextual insights from uploaded resumes, which Amazon Bedrock then evaluates against the job description to generate a candidate summary, skill match score, skill gap analysis, and a shortlist/reject recommendation.
Automated Interview Feedback & Evaluation
Interview transcripts and interviewer ratings are processed using Amazon Kendra for context extraction and Amazon Bedrock for feedback generation, producing candidate strengths and weaknesses, a technical evaluation, a hiring recommendation, and an AI-generated rating score.
Secure, Scalable Architecture
Amazon Cognito manages recruiter authentication, Amazon EC2 orchestrates all workflows, and AWS IAM and AWS KMS enforce role-based access and encryption, with Amazon CloudWatch providing real-time monitoring across the recruitment pipeline.
Business benefits
- Reduced resume screening effort by 70–80% through AI-powered automation
- Achieved a 40–60% faster hiring cycle, significantly improving time-to-hire
- Improved quality of hire through standardized, data-driven candidate evaluation
- Eliminated manual effort in job description creation, screening, and feedback generation
- Enabled unbiased, consistent candidate assessment using AI-driven scoring models
- Enhanced recruiter productivity by automating repetitive tasks
- Improved candidate experience through faster, automated communication
- Centralized recruitment data with real-time visibility and monitoring
Technology stack
