Building an AI-Powered CV Screening System with Explainable Scoring

Nesnyx — Wed, 15 Oct 2025 06:14:19 +0000

Website Demo : https://hireco.intelix.fun/
Github : https://github.com/nesnyx/Hireco-AI

Currently i joined Hackthon and i build this app.

Building an AI Scoring System for Smarter and More Transparent CV Screening

Recruitment can be exhausting — hundreds of CVs come in, and HR teams have to manually scan each one just to find a few strong candidates.
As a developer fascinated by how AI can augment human decision-making, I asked myself:

“Can an AI objectively evaluate CVs based on job criteria — and still explain its reasoning transparently?”

That question became the foundation for my latest project:
🎯 AI Scoring CV Screening System — an intelligent engine that can:

Analyze CVs (PDF/Text)

Score candidates against custom job criteria
Explain the reasoning behind each score
Store results in a structured database
Allow candidate comparison within the same job posting

System Architecture Overview

The system is designed with modular and scalable components:

Frontend (Reactjs Vite) — user interface for uploading CVs and viewing AI results
Backend (FastAPI) — handles upload, analysis pipeline, and LLM integration
LLM Engine (GeminiAI / Local Model) — interprets CVs contextually based on job descriptions
Vector Database (ChromaDB) — stores semantic embeddings for similarity search and clustering
Relational Database (SQLite / Postgres) — keeps structured analysis results and scoring logs

🔍 Before sending data to the LLM, an embedding-based pre-filter reduces unnecessary API calls — saving both cost and time.

Top 3 Improvement Priorities:

Add customizable scoring criteria (e.g., weight for skills, experience, presentation).
Enable candidate comparison dashboards per job posting.
Implement bias and fairness monitoring for ethical scoring.

💡 Lessons Learned

AI is not just about accuracy — it’s about interpretability.
HR users value why a score was given, not just the number.
Efficiency matters.
Embedding pre-filters cut inference costs by up to 70%.
Deployment gotchas teach real lessons.
The infamous attempt to write a readonly database error taught me to handle Docker file permissions carefully.
Human + AI collaboration works best.
The goal isn’t replacing HR — it’s empowering them with better insights.

🛠️ Tech Stack

Backend: FastAPI + Celery (for async jobs)
AI Engine: LangChain / LLM (Gemini or local model)
Databases: SQLite/PostgreSQL + ChromaDB
Deployment: Docker Compose

🌍 Next Steps: Toward an HR-AI Platform

The roadmap ahead focuses on scaling and usability:
Add criteria templates for HR teams
Provide candidate comparison visualization
Offer LLM explainability reports for audit transparency
Integrate with job portals via API

🧩 Reflection

This project isn’t just about automation — it’s about rethinking how AI can assist decision-making.
An explainable scoring system gives HR teams confidence that AI isn’t a “black box,” but a partner in fair evaluation.

🧠 AI should not replace human judgment — it should strengthen it.

💬 Let’s Connect
If you’re working on:

AI explainability
LLM applications in HR or recruitment
Human-AI collaboration design

Forem: Nesnyx

Building an AI-Powered CV Screening System with Explainable Scoring