AI Screening CV

AI Screening CV helps reviewers compare candidates against a role-specific rubric while keeping the evidence visible and editable.

Resume screening can become inconsistent when reviewers rely on quick keyword matching or opaque AI scores.

Create a transparent assistant that extracts relevant experience, maps it to criteria, and supports human hiring decisions.

• Resume upload and parsing service.
• Role criteria configuration with must-have and nice-to-have signals.
• LLM extraction layer that returns structured evidence, not final hiring decisions.
• Reviewer dashboard for notes, status, and shortlist decisions.

• Candidate evidence mapped to job criteria.
• Reviewer notes and status pipeline.
• Missing evidence warnings.
• Prompt and rubric placeholders for role-specific tuning.

• Resume text extraction and section classification.
• LLM-based structured extraction with schema validation.
• Embedding search for matching experience to role criteria.
• Bias and compliance reminder placeholders for human review.

1. 1Recruiter defines role criteria.
2. 2Candidate CV is uploaded and parsed.
3. 3AI extracts experience, skills, and supporting evidence.
4. 4Dashboard shows criteria mapping and review notes.
5. 5Human reviewer decides next step.

• Avoiding opaque scoring and unfair automation.
• Handling diverse resume formats.
• Separating evidence extraction from hiring decisions.

• Use AI as a review assistant, not an automated gatekeeper.
• Expose evidence snippets so reviewers can challenge the output.
• Keep score labels configurable or optional.

Result metrics are intentionally left as placeholders. Add real screening throughput and reviewer feedback after pilot usage.

• Add anonymized review mode.
• Add rubric versioning.
• Add export to applicant tracking systems.