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AI & Machine Learning

AI-Powered Resume Screening: 95% Accuracy in Production

Published
2026-06-20
Author
Satish VishwakarmaSolutions Architect
Reading Time
10 min
Topic
AI & Machine Learning
AI-Powered Resume Screening: 95% Accuracy in Production

How we built an AI resume screener for a Mumbai HR tech startup — architecture, model selection, and accuracy benchmarks.

AI-Powered Resume Screening

A Mumbai HR tech startup needed to automate resume screening for enterprise clients receiving 10,000+ applications per job posting. Manual screening took 40+ hours per role and was inconsistent across reviewers.

System Architecture

Resumes → PDF Parser → Text Chunking → LLM Evaluation → Scoring → Ranking

Processing Pipeline

  1. ›Parse: Extract text from PDF, DOCX, RTF using OCR fallback for scanned documents
  2. ›Chunk: Split into sections (experience, education, skills) using layout-aware segmentation
  3. ›Evaluate: Send each section to GPT-4o with a structured scoring rubric
  4. ›Aggregate: Combine section scores into a weighted overall match percentage

Accuracy Results

  • ›95% accuracy against manual screening benchmarks across 5,000 test resumes
  • ›90% reduction in screening time (40 hours → 4 hours per 1,000 resumes)
  • ›0.92 F1 score for candidate shortlisting — exceeding the 0.85 target

Related: Building a RAG system, LLM fine-tuning vs RAG.

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Building a RAG System for Mumbai Enterprise Document Search

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AI-Powered Resume Screener

projects/ai-resume-screener