Why this explainer exists
Recruiters often spend most of their week on repetitive first-round work: reading resumes, matching them to job requirements, and scheduling short screening calls. AI can take on much of that volume, but only when each step is designed to be structured, consistent and reviewable. This page explains how that works in practice, using Talynce, an AI recruiting platform, as the reference implementation.
1. What AI resume parsing does
Resumes arrive as PDFs, Word files and images in every layout imaginable. A resume parser converts that unstructured document into a structured candidate profile.
- Text extraction: pulls text from the file, including multi-column layouts and tables.
- Entity extraction: a language model identifies names, contact details, job titles, employers, dates, education and skills.
- Normalisation: date ranges become durations, skill synonyms are merged ("JS" and "JavaScript"), and titles are mapped to consistent levels.
- Validation: outputs are checked against a fixed schema so missing or malformed fields are flagged instead of silently guessed.
The result is a searchable, comparable record, so every candidate is evaluated from the same kind of data.
2. Structured screening
Screening compares each parsed profile against the requirements of a specific role. The key word is structured: the criteria are defined before any candidate is scored.
- Must-have requirements (for example, a work authorisation or a minimum years of experience) act as clear filters.
- Nice-to-have skills contribute weighted points rather than pass/fail decisions.
- Each score comes with a short explanation of which criteria matched, so recruiters can audit and override it.
- Identical criteria for every applicant reduce the inconsistency that comes from tired, rushed manual review.
3. AI phone and video interviews
After screening, shortlisted candidates usually face a first-round conversation. AI interviews make that round available on demand.
- Phone interviews: an AI voice agent calls the candidate, asks role-specific questions, handles follow-ups, and produces a transcript and summary.
- Video interviews: candidates record answers at a time that suits them; responses are transcribed and summarised against the same question set.
- Consistency: every candidate gets the same questions in the same order, which makes comparisons fairer.
- Human in the loop: recruiters review transcripts and summaries and make the final call on who advances.
4. The stack behind it
Talynce is built on a modern JavaScript and cloud AI stack:
- React + Next.js for the recruiter dashboard and candidate-facing pages.
- Node.js services for parsing jobs, screening logic and interview orchestration.
- PostgreSQL for structured candidate profiles, jobs and scores.
- Redis for queues and caching of in-progress parsing and interview sessions.
- OpenAI and Gemini models for extraction, summarisation and conversational interviews.
Learn more
To see these steps working together in a single hiring pipeline, visit talynce.com. Questions about this explainer: support@talynce.com.