This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine.
What I Built
Ever wondered how many talented employees are sitting inside an organization, waiting for the right opportunity?
Meet TalentMesh — a skill-based workforce allocation platform designed to uncover hidden talent within organizations.
In many companies, finding the right person for a project is still a manual process involving spreadsheets, endless conversations, and a lot of guesswork.
TalentMesh tackles this problem by connecting employee skills, experience, availability, and project requirements.
Its internal matching algorithm evaluates potential candidates using three factors:
Skills — 50%: How closely an employee's skills match the project requirements.
Availability — 30%: How much of the employee's capacity is available.
Experience — 20%: How well their experience aligns with project needs.
The result? A ranked list of matching candidates, helping managers discover talent that might otherwise remain invisible.
The platform also includes JWT-based authentication, role-based access control, project management, allocation requests, and an approval workflow.
My interpretation of Ghost in the Machine?
The ghost isn't artificial intelligence. It's the hidden potential already living inside your organization.
Demo
Live Demo: https://github.com/praniket01/TalentMesh
Code
GitHub Repository: https://github.com/praniket01/TalentMesh
Built with an open-source technology stack:
Frontend: Angular 21, TypeScript, Signals
Backend: ASP.NET Core / .NET
Database: PostgreSQL
Architecture: Microservices
Authentication: JWT and role-based authorization
How I Built It
TalentMesh follows a microservices architecture, separating responsibilities into independent services:
Identity Service: Authentication and authorization.
Employee Service: Employee profiles, skills, and availability.
Project Service: Project creation and skill requirements.
Matching Service: Candidate evaluation and ranking.
Allocation Service: Allocation requests, approvals, and rejections.
API Gateway: Central entry point for frontend requests.
The matching engine uses a weighted scoring algorithm rather than an AI model.
I deliberately focused on building an explainable, deterministic matching mechanism. Instead of asking a black box to recommend employees, the platform calculates scores using clearly defined business rules.
No LLM. No AI API calls. Just structured data, a matching algorithm, and a little engineering.
Why Does Open Innovation Matter?
TalentMesh is built using open-source technologies, making its architecture and matching logic accessible for developers to inspect, understand, extend, and improve.
Open innovation makes it possible to:
Customize matching strategies for different organizations.
Introduce new scoring factors and allocation policies.
Extend individual microservices independently.
Integrate future open-source AI models without locking the platform into a proprietary AI provider.
The goal is to build something developers can learn from, contribute to, and evolve together.
My Agent Session
Not applicable — no AI agent session was very less used in this project.
The Spirit of It
Every organization has a hidden talent pool.
Someone who has the exact skills needed for the next big project might be buried under a spreadsheet, assigned to unrelated work, or simply overlooked.
TalentMesh is my attempt to make that invisible talent visible.
Because sometimes, the best person for your next project isn't someone you need to hire.
They're already sitting somewhere in your organization. You just haven't found them yet. 👻
Top comments (0)