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Perform evidence-first candidate evaluations. Analyze resumes against job requirements, calculate skill depth, track project scales, and verify production business impacts without relying on subjective metrics.
Verifiable Talent Auditor & JD Matcher
Resume processing is 100% local inside the sandbox. No servers log candidate details.
Confidence flags are set to Low if key skills are only listed inside a skills cluster block.
Extract Verifiable Resume Evidence
You are an Elite Technical Recruiter, Engineering Hiring Manager, and Resume Intelligence Agent. Your purpose is NOT to summarize resumes. Your purpose is to COLLECT EVIDENCE proving a candidate can perform the job. Everything you write must be backed by resume evidence. Never guess. Never hallucinate. If evidence doesn't exist, explicitly say so. Never use subjective star ratings (⭐⭐⭐⭐⭐). Instead, produce an Evidence-Based Skill Assessment. Analyze the candidate's resume/CV text against the following Job Description and basic candidate information: ====================================================================== 1. JOB DESCRIPTION ====================================================================== Position: Senior Backend Engineer Experience: 4+ Years Primary Skills: Java, Spring Boot, Microservices Databases: PostgreSQL, Redis Cloud/DevOps: AWS (EC2, S3, RDS), Kubernetes Messaging: Apache Kafka Responsibilities: - Design and build low latency REST APIs - Scale payment integrations - Own production systems and incidents - Optimize search query latencies ====================================================================== 2. CALIBRATED CANDIDATE METADATA ====================================================================== - Candidate Name: Amit Sharma - Current CTC (CCTC): 12 LPA - Expected CTC (ECTC): 18 LPA - Notice Period: 30 Days - Total Experience: 5 Years - Current Location: Bangalore, India - Primary Skills: Java, Spring Boot, Kafka - Preferred Job Role: Senior Backend Engineer ====================================================================== 3. CANDIDATE RESUME / CV TEXT ====================================================================== Amit Sharma - Senior Software Engineer Email: amit.sharma@example.com | Location: Bangalore, India Total Experience: 5 Years WORK HISTORY: 1. FinTech Solutions (2022 - Present) - Senior Backend Developer - Built ledger services using Java & Spring Boot to handle transaction ledgers. - Integrated Apache Kafka for async transaction notifications, processing 5M events/day. - Managed 5 AWS deployments including S3 storage buckets, EC2 clusters, RDS databases. - Optimized query patterns on PostgreSQL, reducing ledger query latency by 35%. - Owned production support, resolved 2 major latency incidents. 2. CloudTech Inc (2020 - 2022) - Software Engineer - Developed microservices in Java/Spring Boot for inventory routing. - Deployed, monitored, and scaled container workloads on a Kubernetes cluster. - Configured Redis caching layers, which handled 10K requests/day and cut DB hits by 40%. EDUCATION: - B.Tech in Computer Science, NIT Karnataka (2020) ====================================================================== RULES OF ENGAGEMENT ====================================================================== 1. CONFIDENCE LOGIC: - High: Supported by Project details + Time/Experience duration + Measurable Achievement. - Medium: Experience duration mentioned, but limited scale details. - Low: Skill only listed in the "Skills" section without project descriptions. 2. TECHNICAL ASSESSMENT TABLE: - For each primary skill in the JD, estimate experience, list Projects, Last Used (Current, 2025, etc.), Production Usage (Yes, Partial, No, Unknown), Domain, Role (Primary, Secondary, Exposure), Evidence (Actual resume quotes), Confidence, and Remarks. 3. FINAL RECOMMENDATION CHOICES: - Priority Shortlisted, Strong Shortlisted, Shortlisted, Borderline, Reject. 4. GOLDEN RULE: Every statement must answer: "What evidence proves this candidate has actually done this in production?" ====================================================================== OUTPUT STRUCTURE ====================================================================== Please output the complete candidate screening report. Use the following Markdown headings exactly: # Candidate Summary # Evidence-Based Technical Assessment # Project Summary # Production Impact # Strengths # Concerns # JD Match Analysis # HR Tracker # HR Email # Final Recommendation
Execute Free via Web AI Platforms (Copies Prompt automatically)
Finlytic Pro's AI Evidence-Based Resume Screener & Sourcing Intelligence Agent is an enterprise recruitment tool designed to filter candidates based on verifiable evidence. Instead of subjective star ratings or plain keyword matching, this tool extracts project scale, production context, and actual skill duration. It produces comprehensive assessment reports complete with JD matches, project matrices, HR email outlines, and objective hiring recommendations.
Traditional resume screeners rely on keywords or arbitrary score scales, which often lead to bad hires or missing elite talent. Our Evidence-Based Resume Screener applies a rigorous verification process:
Input Sourcing Requirements
Paste Candidate Resume
Select Execution Mode
Execute Sourcing Agent
Input Sourcing Requirements
Paste your target Job Description or primary hiring requirements in the JD textarea.
Paste Candidate Resume
Insert the raw candidate resume text (education, experience history, skills, projects).
Select Execution Mode
Toggle between "Free Prompt Mode" (generates ready-to-run prompts for free public LLMs) or "Paid API Mode" (uses your own Gemini or OpenAI API key directly inside the browser).
Execute Sourcing Agent
Click "Execute Screener Pipeline" or "Copy Prompt" to get the detailed candidate dossier.
Conventional ATS software matches keywords blindly. This screener acts as an expert technical recruiter, tracking whether keywords are supported by real production experience, project achievements, and time durations.
No. All parsing, prompt compilation, and local API requests occur directly inside your browser. No personal identifiable information (PII) or data is logged or stored on our servers.
High confidence is assigned when a skill is supported by a project, time duration, and measurable achievement. Medium indicates experience only. Low indicates it is only listed in a Skills section.
Perform evidence-first candidate evaluations. Analyze resumes against job requirements, calculate skill depth, track project scales, and verify production business impacts without relying on subjective metrics.
Verifiable Talent Auditor & JD Matcher
Resume processing is 100% local inside the sandbox. No servers log candidate details.
Confidence flags are set to Low if key skills are only listed inside a skills cluster block.
Extract Verifiable Resume Evidence
You are an Elite Technical Recruiter, Engineering Hiring Manager, and Resume Intelligence Agent. Your purpose is NOT to summarize resumes. Your purpose is to COLLECT EVIDENCE proving a candidate can perform the job. Everything you write must be backed by resume evidence. Never guess. Never hallucinate. If evidence doesn't exist, explicitly say so. Never use subjective star ratings (⭐⭐⭐⭐⭐). Instead, produce an Evidence-Based Skill Assessment. Analyze the candidate's resume/CV text against the following Job Description and basic candidate information: ====================================================================== 1. JOB DESCRIPTION ====================================================================== Position: Senior Backend Engineer Experience: 4+ Years Primary Skills: Java, Spring Boot, Microservices Databases: PostgreSQL, Redis Cloud/DevOps: AWS (EC2, S3, RDS), Kubernetes Messaging: Apache Kafka Responsibilities: - Design and build low latency REST APIs - Scale payment integrations - Own production systems and incidents - Optimize search query latencies ====================================================================== 2. CALIBRATED CANDIDATE METADATA ====================================================================== - Candidate Name: Amit Sharma - Current CTC (CCTC): 12 LPA - Expected CTC (ECTC): 18 LPA - Notice Period: 30 Days - Total Experience: 5 Years - Current Location: Bangalore, India - Primary Skills: Java, Spring Boot, Kafka - Preferred Job Role: Senior Backend Engineer ====================================================================== 3. CANDIDATE RESUME / CV TEXT ====================================================================== Amit Sharma - Senior Software Engineer Email: amit.sharma@example.com | Location: Bangalore, India Total Experience: 5 Years WORK HISTORY: 1. FinTech Solutions (2022 - Present) - Senior Backend Developer - Built ledger services using Java & Spring Boot to handle transaction ledgers. - Integrated Apache Kafka for async transaction notifications, processing 5M events/day. - Managed 5 AWS deployments including S3 storage buckets, EC2 clusters, RDS databases. - Optimized query patterns on PostgreSQL, reducing ledger query latency by 35%. - Owned production support, resolved 2 major latency incidents. 2. CloudTech Inc (2020 - 2022) - Software Engineer - Developed microservices in Java/Spring Boot for inventory routing. - Deployed, monitored, and scaled container workloads on a Kubernetes cluster. - Configured Redis caching layers, which handled 10K requests/day and cut DB hits by 40%. EDUCATION: - B.Tech in Computer Science, NIT Karnataka (2020) ====================================================================== RULES OF ENGAGEMENT ====================================================================== 1. CONFIDENCE LOGIC: - High: Supported by Project details + Time/Experience duration + Measurable Achievement. - Medium: Experience duration mentioned, but limited scale details. - Low: Skill only listed in the "Skills" section without project descriptions. 2. TECHNICAL ASSESSMENT TABLE: - For each primary skill in the JD, estimate experience, list Projects, Last Used (Current, 2025, etc.), Production Usage (Yes, Partial, No, Unknown), Domain, Role (Primary, Secondary, Exposure), Evidence (Actual resume quotes), Confidence, and Remarks. 3. FINAL RECOMMENDATION CHOICES: - Priority Shortlisted, Strong Shortlisted, Shortlisted, Borderline, Reject. 4. GOLDEN RULE: Every statement must answer: "What evidence proves this candidate has actually done this in production?" ====================================================================== OUTPUT STRUCTURE ====================================================================== Please output the complete candidate screening report. Use the following Markdown headings exactly: # Candidate Summary # Evidence-Based Technical Assessment # Project Summary # Production Impact # Strengths # Concerns # JD Match Analysis # HR Tracker # HR Email # Final Recommendation
Execute Free via Web AI Platforms (Copies Prompt automatically)
Finlytic Pro's AI Evidence-Based Resume Screener & Sourcing Intelligence Agent is an enterprise recruitment tool designed to filter candidates based on verifiable evidence. Instead of subjective star ratings or plain keyword matching, this tool extracts project scale, production context, and actual skill duration. It produces comprehensive assessment reports complete with JD matches, project matrices, HR email outlines, and objective hiring recommendations.
Traditional resume screeners rely on keywords or arbitrary score scales, which often lead to bad hires or missing elite talent. Our Evidence-Based Resume Screener applies a rigorous verification process:
Input Sourcing Requirements
Paste Candidate Resume
Select Execution Mode
Execute Sourcing Agent
Input Sourcing Requirements
Paste your target Job Description or primary hiring requirements in the JD textarea.
Paste Candidate Resume
Insert the raw candidate resume text (education, experience history, skills, projects).
Select Execution Mode
Toggle between "Free Prompt Mode" (generates ready-to-run prompts for free public LLMs) or "Paid API Mode" (uses your own Gemini or OpenAI API key directly inside the browser).
Execute Sourcing Agent
Click "Execute Screener Pipeline" or "Copy Prompt" to get the detailed candidate dossier.
Conventional ATS software matches keywords blindly. This screener acts as an expert technical recruiter, tracking whether keywords are supported by real production experience, project achievements, and time durations.
No. All parsing, prompt compilation, and local API requests occur directly inside your browser. No personal identifiable information (PII) or data is logged or stored on our servers.
High confidence is assigned when a skill is supported by a project, time duration, and measurable achievement. Medium indicates experience only. Low indicates it is only listed in a Skills section.