Introduction
In modern recruitment workflows, resumes remain one of the most critical sources of candidate information. However, resumes are inherently unstructured, vary widely in format, and require significant manual effort to review and input into enterprise systems like Salesforce. This manual process consumes time and introduces inconsistencies and errors in candidate data.
To address these challenges, Here’s an automated resume processing solution within Salesforce. This solution leverages Lightning Web Components (LWC) for a user-friendly interface, JavaScript-based PDF text extraction for handling resume content on the client side, and Large Language Models (LLMs) to intelligently analyze resumes and convert unstructured text into structured, actionable data. The extracted information is then inserted into a Salesforce custom object for immediate use in workflows and reporting, without storing the original PDF in Salesforce.
Solution Overview
The solution automates the end-to-end flow of resume processing, from upload to structured Salesforce records:
- Resume upload via a Lightning Web Component
- Client-side PDF text extraction using a JavaScript library
- Resume analysis using an LLM to generate structured JSON
- Data insertion into a Salesforce custom object
This approach ensures high performance, scalability, and minimal Salesforce storage usage, while providing recruiters with actionable candidate data immediately.
Resume Upload Using Lightning Web Components
A custom Lightning Web Component was developed to allow users to upload resumes directly from the Salesforce UI. The component:
- Supports PDF files
- Provides a simple, intuitive interface
- Performs client-side validation to ensure proper file format
Once the file is uploaded, it is processed entirely on the client side for text extraction. No PDF file is stored in Salesforce, reducing storage overhead and maintaining compliance with data handling policies.
Client-Side PDF Text Extraction
The uploaded PDF is processed using a JavaScript PDF parsing library within the LWC. This extracts the full text content of the resume for analysis. Processing on the client side offers several advantages:
- Reduces load on Salesforce Apex and avoids hitting governor limits
- Improves performance and responsiveness
- Avoids storing sensitive documents in Salesforce
The extracted text contains all candidate information, including personal details, education, work experience, and skills.
Resume Analysis Using a Large Language Model (LLM)
The extracted text is sent to a Large Language Model (LLM) for intelligent parsing. The LLM is prompted to:
- Read and understand the resume content
- Identify key candidate attributes
- Normalize information from different resume formats
By automating this step, we remove the need for manual data entry and ensure consistency across candidate records.
Data Insertion into Salesforce
Once the JSON response is received, it is passed to an Apex method. The Apex logic:
- Deserializes the JSON payload
- Maps the extracted fields to the Salesforce custom object
- Performs validation and error handling
- Inserts the structured candidate data into Salesforce
After insertion, users can optionally be redirected to the newly created record for immediate review. Since the PDF is not stored, only structured data is persisted, ensuring efficient storage use and compliance with data policies.
Error Handling and Performance
The system includes robust error handling to manage both business and technical failures:
- Validation for missing or invalid files
- Controlled Apex error handling to prevent failed transactions
- Clear distinction between logical failures (e.g., missing required fields) and platform-level errors
Performance is optimized by:
- Returning only essential data (record Id) to the LWC
- Avoiding unnecessary reprocessing
- Performing PDF parsing on the client side
This ensures a fast, responsive experience even for large volumes of resumes.
Business Value
This solution provides significant benefits to recruitment teams:
- Automation: Eliminates repetitive manual data entry
- Accuracy: Reduces errors through AI-driven extraction
- Efficiency: Accelerates candidate onboarding and hiring workflows
- Scalability: Handles high volumes of resumes with minimal manual effort
- Immediate Salesforce Integration: Enables reporting, workflow automation, and analytics
By integrating AI-powered resume analysis with Salesforce, recruitment teams can focus on candidate evaluation rather than data entry, enhancing productivity and decision-making.
Related Blogs
For related implementations in Salesforce and B2C Commerce, see:
- Inserting Orders in B2C Cloud Using Agentforce
- Inserting Orders in B2C Cloud Using LWC and External LLM
These blogs demonstrate similar techniques for automating data insertion and AI-driven processing in Salesforce and B2C Commerce environments.
Conclusion
This implementation demonstrates how Lightning Web Components, client-side PDF processing, and Large Language Models can be combined to solve real-world recruitment challenges in Salesforce. By automating resume analysis and structured data insertion, organizations can significantly improve efficiency, accuracy, and user experience, while avoiding unnecessary document storage in Salesforce.
This approach showcases the practical integration of AI within enterprise platforms, providing a scalable and future-ready foundation for intelligent recruitment workflows.
Have any questions? Feel free to drop an email to support@astreait.com We’d be happy to guide you through the approach and possibilities.