A rules-first, explainable decision automation platform that securely reads only relevant candidate data from existing systems and produces transparent shortlists.
Eligify is a B2B SaaS platform designed to automate and transparentize the shortlisting and ranking of students or candidates for placements and hiring. Rather than replacing existing ERPs or HRMS systems, Eligify acts as a decision intelligence layer that:
- Securely connects to existing data sources (Excel, ERP databases)
- Fetches only relevant candidate data (final-year students, placement-opted candidates)
- Applies rule-based eligibility and scoring logic
- Produces ranked shortlists with clear explanations
- Explains why each candidate was selected or rejected
In India (and globally), colleges and companies face critical challenges during placements and hiring:
| Problem | Impact |
|---|---|
| Scattered Data | Candidate data fragmented across Excel sheets and large ERPs |
| Manual Shortlisting | Slow, error-prone, and inconsistent decisions |
| No Transparency | HR teams cannot justify selection criteria |
| Diverse Criteria | Each company has different eligibility requirements |
| No Audit Trail | Compliance and decision history missing |
| Scalability Issues | Manual processes break down with large candidate volumes |
Core Insight: Decision-making does not scale with candidate volume.
- Eligibility rules always decide outcomes
- AI will only assist later (not in MVP)
- No black-box logic
- Never modify the organization's ERP
- Never require schema changes
- Minimal data access (fetch only required fields)
- Every decision must be transparent
- Rule-by-rule breakdown for each candidate
- Audit trail for compliance
- No SQL knowledge required from users
- No ERP restructuring needed
- Guided, familiar UX
- Excel / CSV Upload – Quick file-based data entry
- Read-Only Database Integration – Secure connection to ERPs without schema changes
- Column Mapping UI – Non-technical field mapping
- Dataset Snapshots – Immutable, auditable data snapshots
- Academic year / semester filters
- Batch / passing year selection
- Placement opt-in flags
- Department / branch filters (optional)
- Filters applied at source – Never fetch all students
- Mandatory Rules – Pass/fail eligibility criteria (e.g., CGPA ≥ 7)
- Scoring Rules – Weighted scoring (skill match, internships, certifications)
- Editable & Versioned – Change rules without data loss
- Stored as Data – Not hardcoded
- Apply mandatory rules (exclude failing candidates)
- Score eligible candidates
- Sort by total score
- Generate ranked shortlist
- No randomness. No black box.
- Rule-by-rule breakdown for each candidate
- Score breakdown showing how points were calculated
- Rejection reasons when candidates fail mandatory rules
- Decision audit trail for compliance
- Export shortlisted candidates as CSV / Excel
- Clean, guided user experience
- Organization-based access control
Frontend (Next.js)
↓
Core Platform Backend (Node.js / NestJS)
↓
Dataset Snapshot + Rule Engine
↓
Ranked Shortlist + Explanations
↓
Export / View / Audit Trail
AI services will be added later as a separate layer.
- Framework: Next.js with TypeScript
- Styling: Tailwind CSS
- Components: shadcn/ui
- Why: Professional UI, fast iteration, guided workflows
- Runtime: Node.js
- Framework: NestJS with TypeScript
- ORM: Prisma
- Database: PostgreSQL
- Why: Integration-heavy SaaS, clean architecture, strong typing, scalable APIs
- File Uploads: AWS S3 or Azure Blob Storage
- Structured Data: PostgreSQL
- Library: Auth.js or Clerk
- Model: Organization-based multi-tenancy
- Roles: Admin, Viewer
- Frontend: Vercel
- Backend: Render or Railway
- Database: Managed PostgreSQL
Perfect for demos, pilots, and small institutions.
User uploads spreadsheet
↓
Platform previews rows
↓
User maps columns (CGPA, skills, etc.)
↓
Data normalized and stored as snapshot
For organizations already using ERPs.
Organization provides read-only DB credentials
↓
Platform discovers schema and lists tables
↓
User selects relevant table(s) and fields
↓
Platform applies filters at source:
• Academic year = final year
• Placement opted = true
• Department = [selected]
↓
Filtered data fetched into snapshot
↓
DB connection closed
↓
No new tables created. No SQL written by org. No ERP modification.
Rules are stored as data, making them editable, versioned, and auditable.
{
"field": "cgpa",
"operator": ">=",
"value": 7.0,
"type": "mandatory"
}| Type | Example | Outcome |
|---|---|---|
| Mandatory | CGPA ≥ 7 | Candidate passes or is excluded |
| Scoring | Internship bonus (+10 points) | Affects ranking, not eligibility |
- Apply mandatory rules – Fail or pass candidates
- Calculate scores – Weighted scoring for eligible candidates
- Sort by total score – Ranked shortlist generated
- Store evaluation result – Immutable record for audit
Candidate: Aditya Sharma
Status: ✅ SELECTED (Rank #3)
Rule Breakdown:
✅ CGPA ≥ 7.0 [PASS] Current: 7.8
✅ No Active Backlogs [PASS] Backlogs: 0
✅ Has Required Skills [PASS] Python, SQL, Java
✅ Placement Opted [PASS]
Score Breakdown:
Base Score: 50
+ Skill Match (Python, SQL): +15
+ Internship (6 months): +10
+ Certification (AWS): +5
───────────────────────
Total Score: 80/100
Ranked #3 among 45 shortlisted candidates.
- Create Organization – Multi-tenancy setup
- Upload File or Connect Database – Data ingestion
- Select Candidate Scope – Final year, placement opted, etc.
- Map Fields – Column/field mapping UI
- Create Rules – Define eligibility and scoring rules
- Run Shortlisting – Execute evaluation
- View Ranked Shortlist – Dashboard with rankings
- Click Candidate → See Explanation – Full rule breakdown
- Export Results – CSV / Excel download
UX Must Feel: Guided • Familiar • Safe • Transparent
- Organization-based authentication
- Candidate data ingestion (Excel / CSV & read-only DB)
- Candidate scope filtering (academic year, placement-opted, batch)
- Column and field mapping UI
- Dataset snapshot creation
- Rule-based eligibility engine
- Score-based ranking
- Explainable decision view
- Export shortlisted candidates (CSV / Excel)
- Clean, guided user experience
- AI-driven decision making
- Resume parsing
- ERP write-back
- Interview scheduling
- ATS / HR workflows
- Payroll / compliance features
AI will be added after the core platform is stable as a separate microservice.
- Normalize skills and skill matching
- Parse resumes for skill extraction
- Suggest rule weightages and thresholds
- Detect overly strict or loose criteria
- Generate natural-language explanations
- AI will never override rules
- Language: Python
- Framework: FastAPI
- Models: NLP, embeddings, skill classification
- Architecture: Separate microservice
The MVP is successful if:
✅ Organizations can shortlist candidates in minutes, not hours
✅ No ERP restructuring is required
✅ Decisions are explainable to all stakeholders
✅ Users trust the output and use it in production
✅ Switching back to manual feels painful
Eligify evolves into a decision intelligence layer for hiring:
- Compliance-friendly screening system – Audit trails for regulatory bodies
- Analytics engine – Identify skill gaps and trends
- Trusted infrastructure – Fair, transparent, bias-reducing shortlisting
- Campus placements
- Lateral hiring
- Scholarships
- Admissions
- Internal mobility
- Contractor screening
- ❌ Do not build ATS features – Focus on shortlisting, not full HR workflows
- ❌ Do not let AI decide eligibility – Rules decide outcomes, AI assists only
- ❌ Do not require ERP schema changes – Read-only integration only
- ❌ Do not overengineer infrastructure – MVPs must be lean
- ❌ Do not chase features before trust – Explainability first
- Node.js 18+
- PostgreSQL 14+
- npm or yarn
# Clone the repository
git clone <repo-url>
# Install dependencies
npm install
# Set up environment variables
cp .env.example .env.local
# Run database migrations
npm run db:migrate
# Start development server
npm run dev- PLAN.md – Detailed project specification
- Architecture Guide – (To be added)
- API Documentation – (To be added)
- Database Schema – (To be added)
(Contribution guidelines to be added)
(License to be decided)
(Contact information to be added)
Organization: College/Corporate Hiring
Region: India (Expanding Globally)
Problem Space: Campus Placements, Corporate Hiring, Admissions
Differentiator: Rules-First, Explainable, Read-Only Integration
Built with ❤️ to make hiring transparent, fair, and scalable.