Automating Candidate Selection to Cut Hiring Time

Azati’s microservice, powered by Large Language Models (LLMs), streamlines candidate selection, reducing hiring time and enhancing match accuracy with advanced semantic search and customizable filters.

Discuss an idea

All Technologies Used

FastAPI
FastAPI
PostgreSQL
PostgreSQL
Pgvector
Pgvector
Llama 3.3 70b
Llama 3.3 70b
OpenAI
OpenAI
LangGraph
LangGraph
LangSmith
LangSmith
stella_en_400M_v5
stella_en_400M_v5

Motivation

A company in the HR industry faced costly and time-consuming recruitment processes. Azati developed an automated solution that streamlined candidate selection, reducing time-to-hire by 40% and improving placement accuracy by 25%, allowing for faster team formation and increased project success.

Main Challenges

Challenge 1
Handling diverse resume formats and layouts

One of the significant challenges encountered was the access management process. Access to certain system components and data had to be routed through a specific department, which often resulted in delays. However, the professionalism of the Azati team allowed us to address this issue promptly, significantly reducing delays and improving the overall efficiency of the development and deployment processes.

Challenge 2
Achieving accurate semantic search

Another challenge involved enabling a search system to find candidates based on the context and nuances of a job description, not just keywords. Similar qualifications or job experiences were often described using completely different terminology, making it difficult to align relevant qualifications accurately across diverse resumes.

Traditional search methods often fail to capture these subtle language differences, leading to less relevant matches. Moreover, maintaining high accuracy and speed across large volumes of data was critical to meet client expectations.

Key Features

  • LLM-Powered Information Extraction: Uses Large Language Models to efficiently extract relevant data from diverse resumes.
  • Semantic Search with Vector Storage: Stores resume data in vector format for fast and accurate candidate filtering based on semantic similarity.
  • Customizable Relevance Filtering: Allows users to adjust the weight of criteria like skills, experience, and education for each project.
  • Virtual Assessment Layer: Assesses candidate suitability and provides explanations for project fit.
  • Rapid Candidate Filtering: Enables quick filtering of candidates based on specific job requirements.
  • Scalable Microservice Architecture: Designed as a scalable solution to handle large datasets efficiently.

Our Approach

Identifying Key Challenges and Defining the Solution
We began by analyzing the primary challenges in the client’s recruitment process, particularly the inefficiency of manually screening resumes and the inability of traditional methods to handle diverse, unstructured data formats. Based on this, we identified the need for an automated solution that could quickly process large volumes of resumes while ensuring high matching accuracy.
Leveraging Advanced AI and LLMs for Data Extraction
To address these challenges, we integrated Large Language Models (LLMs) for intelligent data extraction from resumes and project descriptions. This step enhanced our ability to automatically process and understand unstructured text, enabling accurate identification of key qualifications and experiences across varying resume formats.
Enabling Efficient Candidate Filtering with Semantic Search
We further optimized the process by vectorizing the extracted data and storing it in a vector database. This allowed us to implement semantic search, providing a more nuanced approach to candidate filtering. Instead of relying solely on keywords, our system could recognize and match relevant qualifications based on context and meaning, ensuring the most suitable candidates for each project.
Customizable Candidate Evaluation and Virtual Assessment
To ensure even greater precision, we introduced a Virtual Assessment layer powered by LLMs. This tool evaluates each candidate’s suitability for a job by analyzing their profile in the context of the specific project requirements. The system provides detailed explanations of each candidate’s fit, allowing recruiters to make faster, more informed decisions.

Project Impact

By implementing Azati’s AI-powered solution, our client achieved significant improvements in their recruitment process. In under 4 months, key performance metrics showed clear improvements:

40% reduction in time-to-hire: The automated candidate selection process drastically shortened hiring cycles, enabling faster team formation and quicker project starts.

Improved match accuracy through advanced semantic search and customizable filters, ensuring candidates are more precisely aligned with project needs for better outcomes.

Enhanced efficiency through automation, reducing manual effort and human errors, while automated candidate evaluation provides recruiters with transparent, data-driven insights to make better-informed decisions.

These results, delivered quickly and seamlessly, transformed our client’s hiring process, enabling them to build high-performing teams faster and more effectively, with measurable improvements in project success.

Ready To Get Started

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.