40% reduction in time-to-hire
Automated resume screening and semantic matching significantly accelerated early-stage candidate evaluation.
Technologies used
FastAPI
PostgreSQL
Pgvector
Llama 3.3 70b
OpenAI
LangGraph
LangSmith
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 01
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.
#1
Challenge 02
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.
#2
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.
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Schedule a callSolution
01
LLM-Powered Information Extraction
Uses Large Language Models to automatically extract and interpret candidate data from diverse resume formats, capturing key skills, experiences, education, and professional achievements. The system normalizes varying structures and terminologies, ensuring accurate and consistent data for downstream processing.
Key capabilities:
- Automated parsing of multiple resume formats
- Identification of relevant skills, experience, and education
- Data standardization for uniform processing
02
Semantic Search with Vector Storage
Stores resume data in a vectorized format to enable fast, context-aware semantic search. The system goes beyond simple keyword matching, understanding the meaning behind candidate profiles and job requirements, ensuring more precise matches.
Key capabilities:
- Context-aware candidate matching
- Vector-based similarity search
- High-speed retrieval from large datasets
03
Customizable Candidate Filtering
Allows recruiters to fine-tune the importance of different selection criteria, such as skills, experience, education, or certifications. This ensures candidate rankings are tailored to the specific needs of each role or project.
Key capabilities:
- Dynamic weighting of candidate criteria
- Project-specific candidate ranking
- Integration with recruiter preferences
04
Virtual Assessment Layer
Provides an automated evaluation of candidate suitability for a given role. The system generates detailed explanations of why a candidate is a good fit, highlighting strengths, gaps, and contextual alignment, helping recruiters make faster and more informed hiring decisions.
Key capabilities:
- Role-specific candidate scoring
- Detailed suitability explanations
- Data-driven insights for recruiters
Results & business impact
25% improvement in candidate-role match accuracy
Context-aware search consistently delivered more relevant candidates aligned with project requirements.
1000+ resumes processed daily
The scalable microservice architecture handled high data volumes with stable performance.
Higher recruiter productivity
Automation eliminated repetitive screening tasks, allowing HR teams to focus on interviews and closing roles faster.
More consistent candidate evaluations
LLM-powered Virtual Assessment ensured unified, bias-reduced scoring across all applicants.
Improved project staffing reliability
Faster and more accurate selections helped teams fill roles earlier, reducing project delays and resource gaps.
