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Recruitment Validator

Recruitment Data Validation at Scale

FastAPI + PythonVietnamese EdTech & OPM ProviderDays → Minutes

The Problem

A large Vietnamese EdTech & Online Program Management (OPM) provider receives hundreds to thousands of recruitment applications each cycle. Each application contains details submitted by both the student and the partnering university — and these details must match. Previously, this validation was done manually using pure Excel, a process that was not only time-consuming but next to impossible to complete accurately within the tight turnaround of just a few days.

Days → MinutesManual checking reduced from days to minutes with automated mismatch detection
1000+Applications per Cycle
3Severity Levels (Low / Medium / High)
MinutesInstead of Days
0Missed Mismatches

The Solution

The Recruitment Validator is an automated recruitment data validation tool that ingests application data from both the company and the university, cross-references every field, and flags any discrepancies. Each mismatch is clearly marked with what exactly mismatched and rated by severity — Low, Medium, or High — so the operations team knows exactly what needs attention.

High-severity mismatches signal that the company may need to contact the student and/or the university directly to resolve critical discrepancies before proceeding. Medium-severity items require review but may not block the process. Low-severity flags are informational and can often be resolved internally.

How It Works

1

Data Ingestion

Application data from the company and the university is uploaded into the system

2

Automated Cross-Referencing

Every field is compared between the two sources — identifying discrepancies across hundreds to thousands of records

3

Mismatch Detection & Marking

Each mismatch is flagged with a clear description of what mismatched and where

4

Severity Rating

Every mismatch is rated Low, Medium, or High — guiding the team on what to check, escalate, or resolve

Severity Rating System

Not all mismatches are equal. The system classifies each discrepancy by severity so the operations team can prioritise their response effectively.

Low

Informational

Minor discrepancies that can typically be resolved internally without external contact. Examples: formatting differences, minor typos.

Medium

Requires Review

Notable mismatches that need attention but may not block the recruitment process. The team should verify and correct as needed.

High

Escalation Required

Critical discrepancies where the company may need to contact the student and/or the university directly to resolve before proceeding.

MVC Architecture

Built with a Model-View-Controller (MVC) architecture — chosen deliberately for speed and simplicity. The clean separation of concerns made it fast to develop, easy to reason about, and straightforward to deliver results without over-engineering.

Model

Data Layer

Pydantic models for data validation and structure — ensuring type-safe, validated data flows through the entire pipeline

View

Presentation

Jinja2 templates rendering results directly in the browser — the operations team gets instant, readable output without a separate frontend

Controller

Logic

FastAPI route handlers orchestrating file uploads, cross-referencing logic, mismatch detection, and severity rating

Tech Stack

Framework
FastAPI
High-performance Python web framework — async-first, ideal for data processing workloads
Server
Uvicorn
Lightning-fast ASGI server running the FastAPI application in production
Validation
Pydantic
Data validation and settings management — enforcing type safety and structure across all data models
Views
Jinja2
Template engine rendering validation results directly in the browser — no separate frontend needed
Uploads
python-multipart
Form data parsing for handling Excel file uploads from the operations team
Data
Pandas
Powerful data manipulation and analysis — cross-referencing thousands of records efficiently
Excel
openpyxl
Reading and writing Excel files — integrating seamlessly into the existing spreadsheet-based workflow
Deployment
Server-Deployed
Deployed to a dedicated server for reliable, on-demand processing of large application batches

Impact

Speed

What took days of manual Excel checking is now completed in minutes — freeing the operations team to focus on resolution instead of detection.

🎯

Accuracy

Automated cross-referencing eliminates human error — no more missed mismatches in thousands of records.

📋

Clarity

Every mismatch is documented with what went wrong and how severe it is — no guesswork for the team.

📈

Scalability

Handles hundreds to thousands of applications per cycle without performance degradation — ready for growth.

Client Context

The client is a large Vietnamese EdTech & Online Program Management (OPM) provider that works with multiple universities and handles large volumes of recruitment applications. Due to confidentiality, further details about the company cannot be disclosed.

This project demonstrates how targeted automation can transform a manual, error-prone process into a fast, reliable operation — even in complex, multi-stakeholder environments like education recruitment.

Have a Data Problem That Needs Solving?

From days of manual work to minutes of automated validation.

This tool was built to solve a real operational bottleneck — turning an impossible manual process into a reliable, automated workflow. If your organisation faces similar challenges with data validation, reconciliation, or process automation, the guild can help.

🔍Automated mismatch detection
Days to minutes
📊Severity-rated reporting