AI-Driven Recruitment Automation

Success Story

Boosting Efficiency of Recruitment Processes with LLM and GenAI

By implementing this solution, we successfully increased the efficiency of our recruitment processes, saving time and costs while enhancing its ability to find and manage talent.

<$0.1 per CV

processing cost, driving major savings.

Automated resume parsing

with multimodal LLMs in recruitment workflows.

Technologies

Multimodal LLMs

Google Gemini Pro

Vertex AI

Google Cloud Platform (GCP)

Node.js

Google Cloud Functions

REST protocol

BigQuery

Looker Studio

Expertise

LLM

Gen AI

Enterprise Automation

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Selection difficulty

The team was to build a solution that helps to automate and optimize the following tasks: Selecting candidates according to job requirements.

Interviewer discovery

The team was to build a solution that helps to automate and optimize the following tasks: Finding internal interviewers to assist with hiring.

Knowledge centralization

The team was to build a solution that helps to automate and optimize the following tasks: Centralizing knowledge management and planning for the development of company expertise.

Lack of standardization

The challenge was the lack of a standard structure in CVs, making automatic information extraction difficult.

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Phase 1

Skills catalog foundation

Created a catalog of employees’ and candidates’ skills based on CV information.

Phase 2

ERP integration

Integrated the skills catalog into the company’s ERP system.

Phase 3

LLM implementation

Leveraged multimodal language models (LLMs) for automatic text extraction from resumes using Google Gemini Pro on Vertex AI.

Phase 4

Reporting

Built reports using Looker Studio to address knowledge management needs.

By combining multimodal LLMs with a cloud-native platform, we unlocked hidden value in CV data—accelerating hiring, improving talent visibility, and supporting smarter workforce decisions.

Multimodal LLMs

We leveraged multimodal language models (LLMs) for automatic text extraction from resumes using Google Gemini Pro on Vertex AI. LLMs can “understand” the entire document, structuring not only specified knowledge and skills but also other relevant information.

GCP implementation

The solution was deployed on Google Cloud Platform (GCP) using a serverless approach with Node.js and Google Cloud Functions. This simplified deployment and maintenance.

ERP integration

Integration with the ERP system was implemented in two ways:

REST protocol: For real-time parsing (e.g., when a recruiter uploads a new resume or converts a resume into a corporate template).
BigQuery: For offline parsing when indexing an existing CV database.

The new big data platform improved operational visibility, strengthened analytics capabilities, and enabled ML-driven optimization across the retailer’s value chain.

Cost-efficiency

Running the proposed solution costs less than $0.1 per CV, resulting in significant investment savings compared to other approaches.

Intelligent selection

Beyond the initial tasks, the solution enhances candidate selection intelligently by considering all aspects of the resume and vacancy, not just the specified knowledge.

Automation

The solution allows for the conversion of resumes into selected templates, significantly saving the hiring manager’s time.

Knowledge management

The solution enables strategic planning and the development of the company’s expertise by effectively organizing search and knowledge management processes.

Streamlined processes

This approach helps streamline processes, improve decision-making, and empower the team to focus on finding the right talent and fostering expertise within the organization.