You will get AI-Powered Multi-Agent Scholarship Discovery & Recommendation System
Project details
I build AI agents that solve real problems. This multi-agent scholarship system finds, verifies, and ranks 10 scholarships per search—saving hours of manual research for students and advisors. Unlike generic scholarship databases, this system verifies each scholarship against official sources (.edu, .gov, .ca) and provides explainable rankings so users know exactly why each scholarship was recommended.
Built with Python, FastAPI, Next.js, and LLMs (Ollama/Gemini), this project demonstrates production-ready AI agent development. The architecture is modular and scalable—the same pipeline can be adapted for job matching, course recommendations, or content discovery. I deliver clean, maintainable code with documentation, error handling, and deployment support.
Whether you're an educational institution, scholarship portal, or international student service, this system helps students discover opportunities they're genuinely eligible for—quickly and reliably.
Built with Python, FastAPI, Next.js, and LLMs (Ollama/Gemini), this project demonstrates production-ready AI agent development. The architecture is modular and scalable—the same pipeline can be adapted for job matching, course recommendations, or content discovery. I deliver clean, maintainable code with documentation, error handling, and deployment support.
Whether you're an educational institution, scholarship portal, or international student service, this system helps students discover opportunities they're genuinely eligible for—quickly and reliably.
AI Development Type
Deep Learning, Knowledge Representation, Recommendation System, Software MaintenanceAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$100
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 0 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | |||
Model Documentation | |||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Frequently asked questions
About Rabin
AI Video Ads for DTC Brands
Kathmandu, Nepal - 12:15 am local time
What I do:
· Concept and script — built around a hook that stops the scroll, not just a product demo
· Video generation — realistic UGC-style footage or cinematic product spots, whichever fits the brand
· Full assembly — voiceover, captions, and music, delivered ready to upload
I've produced spec ads across pet products and food brands, and I specialise in the part most AI video gets wrong: making it feel native to the feed rather than obviously generated.
If you're running static image ads and want to test video without committing to a full production budget, that's exactly what this is for. Send me your product and I'll show you what a first ad looks like.
Steps for completing your project
After purchasing the project, send requirements so Rabin can start the project.
Delivery time starts when Rabin receives requirements from you.
Rabin works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Gathering
Collect and finalize project requirements, target users, features, and countries/universities to include. Client provides Tavily API key and design preferences.
Backend Development
Build FastAPI backend with all 5 agents (Search, Extraction, Verification, Eligibility, Ranking). Implement database models, caching, and API endpoints.