AI-Powered Resume Screening Platform
Manual CV reviews were consuming 6–8 hours per open role and still producing inconsistent shortlists. We replaced the process with an NLP pipeline that ranks 500+ applications in under 3 minutes, strips demographic bias from scoring, and surfaces explainable candidate rankings directly inside the team's existing ATS. Speed-to-shortlist dropped by 70%.
The Challenge
The client — a Series A recruitment firm with 18 recruiters — faced unsustainable manual CV review against high application volumes, with no objective, repeatable scoring methodology.
- 6–8 hours of manual CV review per open role
- Inconsistent evaluation standards between recruiters
- Keyword-only ATS that missed strong, differently-worded candidates
- High application volumes of 500–2,000 per role
- No objective, auditable scoring methodology
Our Solution
Aavyalabs designed and delivered an NLP screening engine that parses any resume format, scores candidates semantically against the job description, removes demographic bias, and pushes explainable rankings back into the recruiter's existing ATS.
- Ranked shortlist for 500+ applications in under 3 minutes
- Explainable scores recruiters can audit and override
- Bias mitigation that strips demographic identifiers before scoring
- Bi-directional ATS sync inside the existing workflow
Key Features & Capabilities
What we designed, built, and shipped.
NLP Resume Parser
Extracts structured data — skills, experience, education, certifications — from any resume format (PDF, DOCX, HTML).
Semantic Job Matching
Sentence-transformer embeddings match candidate profiles against JD requirements beyond exact keyword overlap.
Bias-Reduced Scoring
Demographic identifiers are stripped before scoring. Every score ships with an explainability breakdown recruiters can audit and override.
ATS Integration
Bi-directional sync with existing Applicant Tracking Systems. Scores and rankings surface directly inside the recruiter's workflow.
Delivery Roadmap
Months 1–2 · Discovery & Data
Data collection, labelling, and baseline analysis against historical hires.
Month 3 · Model Development
Parsing pipeline and semantic scoring engine with explainability.
Month 4 · Integration
Bi-directional ATS connector and recruiter dashboard.
Month 5 · Calibration
Model calibration against historical hire outcomes and bias audits.
Month 6 · Production Launch
Rollout, drift monitoring, and recruiter onboarding.
What We Delivered
Technology Stack
Need to Cut Your Time-to-Shortlist?
Tell us about your hiring volumes and ATS. We'll map an NLP screening engine that ranks candidates objectively — and plugs into the tools your recruiters already use.