Automatic CV Processing Software With ChatGPT

Challenge

Our client, a company developing digital HR solutions, approached us to create an AI-driven CV processing system that would simplify candidate screening and improve match quality.

Recruiters face the challenging task of manually reviewing hundreds of CVs to identify the most suitable candidates. Traditional keyword-based search tools often miss context, making it difficult to find candidates with relevant but differently phrased experience.

The goal was to develop a system that could understand the meaning behind experience descriptions, reduce manual review time, and ensure high-quality, explainable matches.

Solution

We have developed a web-based CV processing and search module powered by Google Gemini 2.5 Pro and the Pinecone vector database. The module transforms CV analysis into a semantic, intelligent process, enabling recruiters to search and filter candidates based on meaning rather than keywords.

The system parses CVs of any format, converts them into structured profiles, and represents each candidate as a dense semantic embedding. Recruiters can then query the system in natural language and instantly receive ranked, explainable candidate recommendations.

Semantic Data Processing

The system automatically parses CVs into structured data fields such as name, skills, experience, education, and positions. Each candidate profile is converted into a dense vector representation that captures semantic context.

These embeddings are stored in Pinecone, allowing fast and accurate similarity-based searches instead of traditional keyword matching.

Natural-Language Search and Recommendations

Using Gemini 2.5 Pro, we implemented a Retrieval-Augmented Generation (RAG) agent that enables natural-language search. Recruiters can ask complex questions, like “Find candidates with cloud security certifications and fintech experience.”

The model retrieves the most relevant candidate profiles, interprets their context, and presents an explainable ranking showing why each candidate was recommended.

Recruiter Dashboard

We designed an intuitive React.js dashboard where recruiters can filter, compare, and save candidate pools. The interface also allows exporting search results and monitoring performance metrics.

The backend is built with FastAPI and deployed as a cloud-native, containerized service, ensuring scalability and performance under high workloads.

Technologies Used

• LLM: Google Gemini 2.5 Pro
• Vector Database: Pinecone
• Backend: Python (FastAPI)
• Frontend: React.js
• Deployment: Cloud-native infrastructure with containerization for scalability and monitoring

Results

The module significantly improved the efficiency and accuracy of candidate screening:

• 70% reduction in time spent reviewing CVs
• Increased matching accuracy compared to keyword-based search tools
• Scalable performance, easily handling thousands of profiles
• Explainable recommendations, improving recruiter confidence and decision quality

The system has been successfully integrated into the client’s recruitment platform, providing a future-proof foundation for intelligent hiring.