The Human Resources Director of a large group had to manually analyze nearly 50 CVs per day for various recruitments, ranging from technical profiles to administrative positions.
Between high-potential spontaneous applications and responses to published job offers, it was becoming difficult to efficiently sort through files, identify talent to follow-up, and respond to within reasonable time-frames.
This workload weighed heavily on her team and caused delays in the hiring processes.
With DINA, pre-qualification is automated:
- Automatic reading and processing of CVs (PDF, Word, email, platforms)
- Extraction of key elements: skills, experience, education, certifications
- Identification of compliance criteria (complete file, presence of a cover letter, major errors)
- Matching with job criteria and internal selection grids
- Ranking of profiles by relevance and generation of a monthly shortlist of qualified profiles
- Detection of promising profiles requiring human validation
- Automatic sending of acknowledgments of receipt
Results obtained:
- Consistent and objective pre-selection, with automatic ranking by job fit
- Improvement of the candidate experience, thanks to faster, more tailored, and professional responses
- Delegation of repetitive tasks to AI, allowing the HR team to focus on interviews, employer branding, and department support
DINA integrates with reception channels (messaging, web forms, third-party connectors) to automatically centralize applications. Thanks to its intelligent parsing engine, it extracts structured information from heterogeneous documents (PDF, Word, emails), applies NLP algorithms to qualify skills, experience, and education, and compares this data to internal benchmarks or job grids via configurable matching logic. Results are exposed via API or placed in HR workflows, triggering automated actions (template responses, ranking, alerts).