Documentation Index
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What is Gen AI Resume Fitment?
Gen AI Resume Fitment is Talview’s AI-powered screening layer that automatically evaluates incoming candidate resumes against the specific competency and skill requirements of a workflow (job requisition) - and returns a structured fitment verdict with an evidence-backed explanation. It is designed to remove subjectivity from early-stage screening, surface the right candidates faster, and give recruiters a consistent, auditable signal - at scale.How It Works
- Configure - Recruiter or Master Recruiter sets Essential, Preferred, and Optional criteria against the workflow as per the job role requirements
- Parse - System extracts structured data from each incoming resume across four dimensions:
Dimension What is Extracted Used For Personal Information Name, contact details, location Candidate identification and profile creation; not used in fitment scoring Education Details Degrees, institutions, years of completion Evaluated against Essential criteria (e.g., minimum degree requirement) Experience Details Job titles, companies, tenures, responsibilities Primary signal for Essential and Preferred criteria - seniority, recency, domain depth Qualifications & Certifications Listed certifications, professional credentials Evaluated against Essential or Preferred criteria where specific credentials are required - Evaluate - LLM-backed engine returns Strong / Potential / Low Fit with evidence-backed explanation, visible on the candidate card. Please check the demo below:
Configuration Criteria
| Criteria Type | Definition |
|---|---|
| Essential (Must-have) | Non-negotiable requirements the candidate must demonstrate |
| Preferred (Good-to-have) | Attributes that strengthen candidacy; not disqualifying if absent |
| Optional (Nice-to-have) | Supplementary signals that add positive weight; absence never penalises |
| Categories | Criteria |
|---|---|
| Essential | Java or Python, 5+ years backend, distributed systems, B.E./B.Tech, Tier-1 graduation college |
| Preferred | AWS/GCP, AI-led development, microservices, team leadership |
| Optional | Open-source contributions, fintech domain, product company background |
Fitment Tier Definitions
| Tier | When It Applies |
|---|---|
| Strong Fit | All essentials fully met with recent, unambiguous evidence; most preferred met. Ready to contribute with minimal ramp-up. |
| Potential Fit | All essentials met (at most one partially); at least 40% of preferred met. Minor gaps addressable through interview. |
| Low Fit | One or more essentials not met, or two+ essentials only partially met. Material domain, seniority, or skill mismatch. |
What Makes Talview’s Resume Fitment Distinctive
| Capability | Generic AI Screeners | Talview Gen AI Resume Fitment |
|---|---|---|
| Customer-defined criteria | Pre-trained taxonomies | Per-workflow Essential / Preferred / Optional |
| Explainability | Score or summary | Evidence-traced verdict per criterion |
| Hallucination guardrails | Variable | Evidence-only; flags gaps over inflated scores |
| Bias controls | Often opaque | No protected attributes used; criteria-based evaluation |
| Compliance posture | Variable | Aligned to EU AI Act high-risk system requirements |
Trust & Governance
- Evidence-grounded - No inferences beyond explicit resume content; if a criterion cannot be verified, the system states the gap rather than assuming
- Bias-controlled - Evaluates only against your configured criteria; protected attributes (gender, race, age, ethnicity, disability) are not used
- Compliance-aligned - Designed for EU AI Act high-risk system standards: transparent reasoning, configurable criteria, mandatory human oversight, and auditability
- Human-in-the-loop - The AI classifies and explains; recruiters and hiring managers retain full override authority
Frequently asked questions
Does the AI automatically reject candidates?
Does the AI automatically reject candidates?
No. The AI classifies and explains; all shortlisting decisions are made by the hiring team.
Can I upload resumes in bulk?
Can I upload resumes in bulk?
Yes. Resumes can be uploaded individually or in bulk. Candidate profiles are auto created, and fitment runs automatically once criteria are configured.
Can criteria be changed mid-cycle?
Can criteria be changed mid-cycle?
Yes. Criteria can be updated at any time and re-applied to existing candidates in the workflow.
Is candidate data used to train the model?
Is candidate data used to train the model?
No. Talview does not use individual customer data for model training without explicit consent and appropriate anonymisation.
For configuration support or fitment calibration queries, contact your Talview Customer Success Manager.