Inefficiencies in Candidate Screening and Matching
The client operates a digital job marketplace that connects employers and job seekers across multiple industries. The platform enables candidates to create profiles, upload resumes, and apply for positions, while employers can identify and engage talent through both manual search and AI-powered recommendation capabilities.
As hiring volumes increased, the organization faced growing challenges in accurately identifying qualified candidates and maintaining consistency across recruitment workflows.
- Extracting meaningful insights from unstructured CVs: Candidate resumes were often submitted as free-text PDF documents, making it difficult to reliably identify relevant qualifications and experience.
- Identifying strong candidates with incomplete profile data: Many candidate profiles lacked structured information, limiting the effectiveness of traditional search and filtering methods.
- Applying recruiter expertise consistently at scale: Recruitment teams needed a more standardized way to apply hiring logic, business rules, and evaluation criteria across candidate ranking workflows.
An AI-powered matching platform was required to improve candidate analysis, automate profile interpretation, and support more consistent recruitment decisions.
Developing an AI-Powered Candidate Matching Platform
Addressing these challenges required more than traditional applicant tracking workflows. A platform capable of understanding candidate experience contextually, ranking applicants intelligently, and supporting recruiters with real-time recommendations was essential.
Our team engaged as a strategic engineering partner to design and implement an intelligent AI-driven recruitment platform.
1. Building Semantic Candidate Matching Capabilities
Our team developed a standalone microservice that combines structured filtering, LLM-powered contextual analysis, and vector similarity scoring to match candidates with job requirements more intelligently.
A modular proof-of-concept architecture enabled rapid validation of AI-driven recruitment workflows while maintaining flexibility for future expansion.
2. Building a Structured Filtering Engine
The platform applies configurable business rules related to location, remote work eligibility, and additional hiring criteria using both structured candidate profiles and extracted CV information.
A rule-based filtering layer improves consistency across recruitment workflows while ensuring alignment with organizational hiring requirements.
3. Enabling Contextual Candidate Analysis with LLMs
Large language models were implemented to interpret candidate experience, seniority, and domain expertise directly from free-text PDF resumes.
Contextual AI analysis allows the platform to understand qualifications beyond simple keyword matching, improving the identification of relevant and high-potential candidates.
4. Applying Vector Similarity Scoring for Intelligent Matching
Advanced vector similarity algorithms calculate relevance scores by measuring semantic alignment between job requirements and candidate profiles.
Semantic matching improves recruitment accuracy by identifying candidates whose experience and capabilities align contextually with hiring needs.
5. Ensuring Safe Development and Deployment
The entire solution was developed within a cloned non-production database environment to ensure zero disruption to live recruitment operations.
A secure development strategy enabled rapid experimentation and testing while protecting operational recruitment systems and data integrity.
6. Designing an Industry-Agnostic and Scalable Architecture
The platform was engineered to support scalability across multiple industries and future deployment across the client’s broader recruitment ecosystem.
A flexible architecture positions the organization to continue expanding AI-powered recruitment capabilities across additional job categories and hiring workflows.
“The project went beyond building a recommendation algorithm. Our team worked closely with the client to translate recruiter expertise, hiring logic, and decision-making patterns into measurable AI behavior, creating a matching engine capable of understanding candidate relevance in a far more contextual and human-like way.”
Filip Momot
Data Scientist – Addepto
Modernizing ASR Workflows for Better-Organized Trip Execution
The partnership between Addepto and the client transformed traditional recruitment workflows into an intelligent and scalable AI-powered talent matching platform. Recruiters can now identify high-potential candidates faster, reduce manual screening effort, and improve hiring consistency across recruitment operations.
Semantic matching and AI-driven ranking capabilities improve candidate-job alignment while helping recruitment teams process significantly larger applicant pools more efficiently.
Before
- Rule-based keyword matching
- Reliance on structured profile fields only
- Static and predefined matching logic
After
- AI-driven semantic candidate understanding
- Intelligent analysis of unstructured PDF resumes
- Dynamic relevance scoring powered by vector embeddings
The automated workflows also reduce administrative overhead and allow recruiters to focus more on strategic talent engagement and relationship-building activities.
As part of KMS Technology, Addepto continues to deliver enterprise-grade AI solutions that help organizations optimize complex operational workflows through practical and business-focused AI innovation.
Ready to transform recruitment with AI? Contact us today!