NaniFit: AI Applicant Screening & Talent Intelligence Platform
Executive Overview
NaniFit is an autonomous recruitment intelligence and candidate screening platform that utilizes custom AI algorithms and multidimensional radar analysis to objectively evaluate applicants across 9 behavioral and technical dimensions - automating hiring from inclusive JD generation to final panel consensus.
The Core Problem & Architectural Bottlenecks
- The Resume Inundation Crisis: High-growth scale-ups and global enterprises receive thousands of applications per open position. Recruiters spend an average of 40+ hours per requisition manually scanning PDFs, resulting in recruiter burnout and arbitrary 6-second resume triage.
- Keyword ATS Blindspots & Unconscious Bias: Legacy keyword-matching ATS tools disqualify exceptional non-traditional talent who lack exact buzzwords while rewarding keyword-stuffed resumes. Human screeners inadvertently introduce pedigree, gender, and demographic bias into initial evaluations.
- Data Privacy & GDPR Exposure: Uploading un-redacted candidate resumes and sensitive PII into public commercial AI APIs creates catastrophic compliance violations, data leakage, and regulatory exposure.
- Unstructured Hiring Decisions: Hiring panels lack unified, quantitative evaluation rubrics, leading to subjective, unstructured interview feedback and poor hiring consensus.
The Engineering Solution
- 9-Dimension Radar Candidate Profiling: Evaluates technical competencies, problem-solving, communication, culture addition, and behavioral signals with weighted rubric scoring.
- AI-Powered Job Description Generator: Context-aware LLM pipeline that creates inclusive, structured job specs with built-in bias controls.
- Drag-and-Drop Form & Rubric Builder: Interactive canvas for configuring custom screening questions with dynamic scoring weights.
- Collaborative Hiring Panel & Blind Review: Blind candidate evaluations that eliminate demographic markers before aggregate scoring.
- Candidate Talent Pool & Semantic Discovery: Instant talent re-matching using vector similarity across historical applicant pools.
Measurable Business Impact & ROI
- 74% reduction in cost-per-hire across technical engineering and operations roles.
- 8x acceleration in candidate shortlisting velocity compared to manual recruiting workflows.
- 9-dimension objective radar charts providing transparent scoring evidence to hiring managers.
- 300+ automated screening workflows executed per recruiting campaign.
Technology Stack
- Frontend: Next.js 14/15 / React / Tailwind CSS / Framer Motion / Recharts
- AI Core & Backend: Python 3.11 / FastAPI / Custom RAG Pipeline
- Persistence: PostgreSQL / pgVector
- Integrations: Stripe / Telebirr Multi-Currency Billing / Twilio SMS / Email Notifications