"Recruit-AI" Development for Talent Acquisition

The Case Study: "Recruit-AI"
Target User (Persona):
• Sarah, a Talent Acquisition Manager or Founder at a 50-person tech company. She is overwhelmed by administrative tasks and fears missing out on great talent because resumes are buried in PDFs.
• The Problem:
High-volume resume screening leads to decision fatigue, slow time-to-hire, and poor candidate experience.
• The Solution:
An Agentic AI solution that automates the Screening & Scheduling phase of hiring.
• Why Agentic AI:
This problem requires parsing unstructured text (resumes and emails), making judgment calls based on job criteria, and taking actions such as scheduling.

2. Your Mission
Phase 1: Discovery & Strategy
• Market Research
• Problem Framing
• Process Documentation
Phase 2: The Sales Pitch
• Problem Hook
• Market Opportunity
• Solution Overview
• Prototype
• Technical Architecture
• Roadmap & Financials
• Success Metrics
Phase 3: The Build (Frontend & Backend)
Frontend:
• Web interface to upload Job Description and Resume

Backend (Agent):
• Resume analysis against JD
• Structured score (0–100)
• Summary and Recommended Action
• Optional calendar or email integration

Integration:
• Connect frontend and backend or document API handshake
Phase 4: Demo & Reflection
• 5-minute recorded walkthrough
• Reflection on trade-offs, failures, and architecture decisions

3. List of Deliverables
1. Problem Framing Document (1–2 pages)
2. Pitch Deck (5–8 slides)
3. UX / Wireframes
4. Frontend Prototype Link
5. Backend Agent Workflow
6. Metrics & Roadmap Document (1 page)
7. Process & Decision Log
8. 5-Minute Video Pitch