Intelligens
AI-native hiring intelligence powered by Claude
Intelligens transforms fragmented recruitment workflows into a standardized, evidence-driven hiring process with Claude at the center of a multi-agent evaluation system.
Executive Summary
Intelligens is an AI-native Hiring Intelligence Platform that transforms fragmented recruitment workflows into a standardized, evidence-driven hiring process. Using Claude as the central reasoning engine, we built a multi-agent system that automates candidate screening, conducts adaptive interviews, evaluates technical and behavioral competencies, and generates explainable hiring recommendations while keeping recruiters in control.
Instead of replacing recruiters, Claude augments their decision-making capabilities and enables organizations to scale hiring without sacrificing quality.
The Challenge
Organizations hiring at scale faced several severe operational bottlenecks:
- Manual review of hundreds of resumes per role leading to high recruiter fatigue and bias.
- Inconsistent and subjective first-round screening interviews across candidates.
- Inability to gather objective, standardize signals for soft skills and technical competencies.
- Slow time-to-hire cycles resulting in top candidates accepting competing offers.
- High operational overhead and recruiter dependencies for early-stage coordination.
Organizations needed a highly scalable, automated system that could objectively evaluate candidates while preserving rigorous human oversight and data privacy.
Solution Overview
We designed and implemented a Claude-centered multi-agent hiring intelligence architecture. This system orchestrates multiple specialized AI agents, each designed for a specific phase of candidate evaluation, leading to a unified candidate score and review profile.
Claude-Powered Agents
The platform utilizes a team of specialized agents designed to handle specific evaluation workflows:
| Agent Role | Claude Responsibilities | Core Output |
|---|---|---|
| Resume Intelligence Agent | Extracts complex skills, verifies experience, and assesses relevancy against job descriptions. | Candidate Relevancy Summary & Skill Matrix |
| Adaptive Interview Agent | Conducts dynamic, adaptive voice-based screening interviews using real-time conversational AI, personalised per candidate resume and role. | Interview Transcript & Behavioral Signals |
| Technical Evaluation Agent | Reviews code assessments, designs custom coding questions, and scores technical problem-solving. | Technical Code Review & Capability Rating |
| Behavioral Intelligence Agent | Analyzes soft skills, situational judgment answers, and cultural alignment indicators. | Soft Skills Scorecard & Communication Profile |
| Hiring Decision Agent | Consolidates signals from all agents, cross-checks rubrics, and generates explainable recommendations. | Unified Scorecard & Final Recommendation |
Why Claude
Claude was selected as the foundational model for the platform because it excels at:
Long-Context Understanding
Reads and reasons flawlessly across extensive resumes, full interview transcripts, complex rubrics, and recruiter notes.
Multi-Step Reasoning
Maintains context and connects discrete evaluation signals across screening, technical tests, and final recommendations.
Natural Conversations
Supports fluid, context-aware screening dialogues instead of rigid, script-like Q&A sequences.
Structured Outputs
Consistently produces clean, structured JSON schemas containing scores, justifications, and bullet points for recruiter review.
Human-in-the-Loop Design
Recruiters remain the final decision-makers. The AI acts as a co-pilot, enhancing their capability:
- AI Proposes, Humans Validate: Recruiter dashboards show clear, step-by-step reasoning behind every AI rating, allowing quick verification.
- Interactive Overrides: Recruiters can override any AI-generated score or recommendation, feeding corrective signals back to the model.
- Collaborative Notes: Recruiters can add contextual nuances (e.g., "excellent cultural fit during site visit"), which the Hiring Decision Agent automatically incorporates into the final scorecard.
AI Scorecard Example
Below is an example of the structured, explainable scorecard output generated by the Hiring Decision Agent for recruiter review:
Claude Evaluation Report
Verified Candidate Assessment Model
Technology Stack
Frontend
- React 18
- TypeScript
- Vite
Backend
- Node.js
- Express
- Socket.io
AI Layer
- Claude API
- Retell AI
- n8n Workflows
Infrastructure
- MongoDB
- JWT Auth
- AWS S3
Business Outcomes
Screening Effort
-75%
Reduction in manual resume screening time
Hiring Velocity
5x
Faster candidate progression through funnel
Recruiter Output
3.2x
Increase in candidates managed per recruiter
Evaluation Quality
94%
Accuracy in predicting onsite interview success