Overview
The AI Resume Screener eliminates the hours recruiters spend manually comparing resumes to job descriptions. Load a job posting in three different ways, upload a batch of resumes, and receive a ranked evaluation of every candidate — complete with scores, strengths, gaps, and tailored interview questions — in seconds.
How It Works
- Load the job description — Paste text directly, upload a TXT, DOCX, or PDF file, or provide a URL and let the AI scrape and extract the job posting automatically.
- Upload resumes in bulk — Drag and drop or browse for multiple PDF, DOCX, or TXT files. PDF and TXT are parsed in your browser; DOCX is converted on our server in-memory and discarded immediately after text extraction. In all cases the files themselves are never stored.
- Run the analysis — The tool evaluates every candidate against the job requirements and returns a ranked list with scores, recommendations, and a comparison narrative.
- Drill into candidates — Expand any candidate to see their score breakdown (skills, experience, education), key requirements met, and a strengths-vs-gaps analysis.
- Generate interview questions — Click the interview button on any candidate card to get 15–20 tailored questions grouped by category, each with a rationale explaining why it's relevant to that candidate.
Scoring Guide
| Score | Label | Interpretation |
|---|---|---|
| 85 – 100 | Strong Fit | Meets or exceeds most requirements |
| 70 – 84 | Good Fit | Solid match with minor gaps |
| 55 – 69 | Partial Fit | Some relevant experience, notable gaps |
| < 55 | Poor Fit | Significant misalignment |
Resume File Formats
The screener accepts:
- PDF — Parsed client-side using PDF.js
- DOCX — Parsed server-side (in-memory) using Mammoth; the file is discarded immediately after text extraction
- TXT — Read directly in the browser
Up to 10 resumes can be analyzed per run on the Basic tier (25 on Advanced).
Interview Question Categories
Generated questions are organized into five categories:
- Technical Skills — Role-specific technical depth probes
- Experience & Accomplishments — Past work and measurable impact
- Behavioral — STAR-format questions tied to stated requirements
- Situational — Hypothetical scenarios relevant to the role
- Cultural Fit — Alignment with team and organizational values
Bias-Aware AI Scoring
A default-off "Apply EEOC-aligned anti-bias rules" toggle prepends a strict instruction set to the AI prompt: ignore protected-class attributes (race, gender, age, disability, religion, national origin, marital/parental status, military/veteran status, genetic info) and their proxies (school name, neighborhood, names suggesting cultural origin, gap patterns reflecting caregiving / military / illness), avoid "cultural fit" as a scoring factor, and don't penalize career gaps without role-relevant evidence. Aligns with U.S. EEOC, UK Equality Act 2010, EU Equal Treatment Directive, and Canadian Human Rights Act frameworks for AI-assisted hiring.
Recruiters in regulated industries or jurisdictions with strict anti-discrimination requirements can opt in; users in lighter-touch jurisdictions can leave it off. Server-side preamble — does not change the response shape.
Use Cases
- High-volume applicant screening for in-house recruiting teams
- Rapid shortlisting during active hiring campaigns
- Interview prep packages for hiring managers
- Competitive candidate benchmarking across multiple roles
- Freelance recruiter efficiency tool for client engagements
From Demo to Production
This demo screens up to 10 resumes at a time against a single job description. A production deployment integrates with your ATS, scales to your hiring volume with batch processing, runs bias audits, and fits into your existing recruiting workflow.
Real-World Challenges
| Challenge | Why It's Hard |
|---|---|
| Resume parsing quality | Multi-column PDFs, images-as-text, and non-standard formatting break standard parsers. Production needs multiple extraction strategies with fallback. |
| Bias and fairness | AI must not discriminate by name, age, gender, or school prestige. Requires ongoing auditing and model guardrails. |
| ATS integration | Most companies want screening inside their existing workflow (Greenhouse, Lever, Workday), not a separate tool. |
| Candidate experience | Applicants want to know they were fairly evaluated. Transparency and explainability matter for employer brand. |
| Legal compliance | NYC Local Law 144, EU AI Act, and emerging regulations require bias audits for automated hiring tools. Non-compliance = fines and lawsuits. |
| Calibration drift | As roles evolve, scoring criteria need regular updates. A model tuned for 2025 job descriptions may misjudge 2026 requirements. |
Cost Estimates
| Component | Starter | Growth | Enterprise |
|---|---|---|---|
| AI API (GPT-4o-mini / GPT-4.1) | $50–200/mo | $200–800/mo | $800–3,000/mo |
| ATS integration (Greenhouse, Lever, Workday Recruiting) | $100–400/mo | $400–1,500/mo | $1,500–5,000/mo |
| Bias audit and compliance | $0–500/quarter | $500–2,000/quarter | $2,000–10,000/quarter |
| Total monthly | ~$100–500 | ~$500–2,500 | ~$2,500–10,000 |
ROI Definition
- Primary metric: Recruiter time saved (target: 80–90% reduction in initial screening time)
- Secondary metric: Quality-of-hire improvement from consistent, criteria-based evaluation
- Break-even: Typically within 1 month for teams screening 50+ candidates per role
- Concrete example: Recruiter screening 200 resumes at 5 min each = 16.7 hours. AI screens in minutes, recruiter reviews top 20 in 1.5 hours = 15 hours saved per role. At 10 open roles/month and $45/hr recruiter cost = $6,750/month saved vs ~$800/month tool cost
Technology Stack
- AI Model: OpenAI GPT-4o-mini (standard) / GPT-4.1 (advanced mode, with model selection)
- Backend: Next.js API route (serverless)
- Frontend: React client with drag-and-drop file upload
- Resume Parsing: PDF.js (PDF, client-side) + Mammoth (DOCX, server-side, in-memory)
- Job Posting Input: URL scraping + manual paste + file upload
Want This for Your Business?
Production deployment with ATS integration, bias audit reporting, and multi-role batch screening. Fits into your existing recruiting workflow. A full deployment typically takes 2–4 weeks and starts at $4,000.
This demo uses GPT-4o-mini. All AI evaluations should be treated as decision-support tools and reviewed by a qualified recruiter or HR professional. Do not upload resumes containing sensitive personal information beyond what is needed for evaluation.