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. Text is extracted client-side so your files 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 client-side using Mammoth
- TXT — Read directly in the browser
Up to 10 resumes can be analyzed in a single run.
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
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-4o) | $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-4o (advanced mode)
- Backend: Next.js API route (serverless)
- Frontend: React client with drag-and-drop file upload
- Resume Parsing: PDF.js (PDF) + Mammoth (DOCX), client-side
- 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.