Enterprise automates scalable resume processing for high-volume recruitment
Yearling AI developed a scalable resume processing solution for enterprises and job search portals that automates bulk resume parsing, reducing manual labor and time in extracting information from potentially hundreds of thousands of resumes.
The Challenge
Resume processing in bulk is a tedious, time-consuming, and difficult task for Human Resource personnel. Medium and large enterprises receive thousands of resumes every month for a variety of job openings, while job search portals process even larger volumes.
The solution was developed based on requirements from two enterprise customers via Yearling AI's consulting partner on Google Cloud Platform, with the goal of eliminating manual resume processing for hundreds of thousands of resumes.
Key Requirements:
- Parse resumes and store key terms for easy searchability
- Extract sections like Education, Experience, Skills, and Contact Info
- Identify named entities (universities, companies) for quick lookup
The Solution
Yearling AI developed an end-to-end machine learning pipeline for resume processing that automates the entire workflow from text extraction through searchable data storage.
ML Pipeline Steps:
Detect and extract text from PDF or DOC resume files using OCR technology
Generate text embeddings for sentences/paragraphs using NLP-based vectorization
Use unsupervised learning to group text into sections like Education, Experience, and Contact Info
Identify entities such as organizations and locations within each section
Store original text, extracted information, and embedding vectors in a datastore
Build global and intra-resume search indexes for efficient keyword and phrase searches
The Results
This solution removes the manual data-entry bottleneck from high-volume recruitment pipelines. Recruiters stop re-reading raw resumes to extract structured data and instead query a searchable candidate database that is built and updated automatically.
Customer Benefits:
Current Status
Currently being demonstrated to both enterprise clients, with a software license agreement in progress. The solution has proven its value in automating one of the most time-consuming tasks in recruitment operations.
Project Overview
Technologies Used
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