StaffUp
How we cut candidate screening time 70% with an AI matching engine for StaffUp
AI Recruitment SaaS with Intelligent Matching Engine
Built StaffUp, a serverless recruitment SaaS powered by a proprietary 7-criteria scoring engine using Next.js 15 and Firebase. Automated candidate ranking through availability matching, psychometric alignment, certification validation, and experience weighting. Reduced manual screening time by 70%, introduced real-time dashboards for employers and admins.
Screening time reduced
Dashboards
Industry
Recruitment & HR Tech
Client
High-Volume Seasonal Staffing
Engagement
End-to-end Serverless SaaS Architecture
Outcome
70% reduction in manual screening time
Tech Stack
Next.js 15 (App Router), React 19, Tailwind CSS, Firebase Admin, Firestore, Redux Toolkit, Resend API
A look inside the live platform — scroll to explore →
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The Core Problem
Traditional hiring relied on manual resume screening, subjective judgment, and reactive filtering. Employers were overwhelmed by unqualified applicants, while strong candidates were overlooked due to availability or documentation mismatches.
The client needed a data-driven system that could automatically rank candidates by job fit in real time.
01.
Resume fatigue and slow manual review
02.
Availability mismatches for fixed seasonal dates
03.
No objective scoring for psychometric alignment
04.
Lack of transparency for candidates
05.
No structured ranking logic for employers
Our Solution: 7-Criteria Scoring Engine
We built a proprietary scoring engine that calculates match percentage dynamically. The system generates rank-ordered employer views, eliminating resume scanning.
•
Availability overlap algorithm
•
Psychometric benchmark comparison
•
Quantified seasonal experience
•
Certification validation checks
•
Profile completeness weighting
Intelligent Candidate Feedback
Instead of rejecting applicants silently, the system generates automated improvement roadmaps.
• Complete required training modules
• Upload missing certifications
• Improve psychometric threshold score
This increases talent quality over time and gamifies professional growth.
Multi-Role Dashboard Architecture
We designed three separate experiences:
Employer Dashboard
Real-time rank and filter system for top-fit candidates.
Admin Command Center
Performance analytics, growth visualization, placement insights.
Candidate Portal
Personalized job feed sorted by match percentage.
Technical Architecture
Frontend
Next.js 15 (App Router), React 19, Tailwind CSS
Backend
Next.js Server Actions with Firebase Admin
Database
Firestore with real-time mutations
State & Email
Redux Toolkit, Resend API for transactional messaging
Architecture Model: Serverless, horizontally scalable, low-maintenance infrastructure.
Results
A structured data-driven recruitment engine that replaced manual processes with scalable, automated systems.
0%
Screening time reduced
Manual review cut by the scoring engine
0 roles
Dashboards
Employer, admin, and candidate views
0%
Transparency
Candidate ranking explained in real time
0 engine
Matching system
Availability, scores, and feedback unified
Why This Matters
This wasn't a job board. It was a structured data-driven recruitment engine built with custom scoring algorithms, serverless SaaS architecture, automated ranking systems, and real-time data mutations.
At TechEmulsion, we design intelligent SaaS platforms that replace manual processes with scalable, automated systems. If you're building a recruitment tech product, matching engine, or data-driven SaaS platform, we can architect it end-to-end.