Nexus-Intelligence

NEXUS is a data-driven radar for internships. It replaces lists with quality-scored nodes, using explainable signals to help students make fast, confident career decisions over endless scrolling.

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Inspiration

The inspiration for Nexus-Intelligence came from the frustration of navigating traditional internship portals that overwhelm students with duplicated, low-quality, and poorly ranked listings. While opportunities exist in abundance, there is no system that helps students understand which ones are actually worth pursuing. I wanted to move beyond simple aggregation and build something that treats internships as signals that can be analyzed, ranked, and explained, helping students make confident, informed decisions instead of endlessly scrolling and guessing.

What it does

Nexus-Intelligence is an internship intelligence platform that aggregates opportunities from multiple sources, cleans and deduplicates noisy data, and indexes them into a powerful search engine. Each internship is scored using explainable signals such as recency, role clarity, and location flexibility. The frontend presents these opportunities through a radar-style interface where users can search, filter, compare, and clearly see why one internship ranks higher than another, transforming browsing into decision-making.

How we built it

The project was built as an end-to-end data pipeline. Internship data is scraped from the web, stored as raw JSON, then cleaned and deduplicated using realistic validation rules. The cleaned data is indexed into Elasticsearch to enable fast search and filtering. An intelligence layer computes a quality score for each opportunity, and a React-based frontend visualizes this data using a cinematic, data-driven UI. The backend is designed to be modular, scalable, and explainable.

Challenges we ran into

One of the biggest challenges was dealing with inconsistent and incomplete real-world data. Overly strict validation rules initially removed valid internships, requiring multiple iterations to balance data quality and data retention. Setting up and debugging Elasticsearch locally was another challenge, as was ensuring the scoring system remained transparent rather than a black-box.

Accomplishments that we're proud of

We successfully built a fully working internship intelligence pipeline from scratch, integrating scraping, cleaning, search indexing, and explainable scoring. The radar-style UI and comparison mode stand out as a unique approach that goes beyond standard job boards. Most importantly, the system provides clear reasoning behind rankings, which is rare in student-focused platforms.

What we learned

This project taught us how real data systems behave in practice. We learned the importance of separating ingestion from enrichment, designing validation rules that reflect messy reality, and using search engines for scalable retrieval. We also gained valuable experience in building explainable intelligence rather than opaque recommendations.

What's next for Nexus-Intelligence

Next, we plan to add more data sources, enrich listings with skill extraction and stipend estimation, and introduce personalized scoring based on user preferences. We also aim to enhance the UI with real-time trend detection and deploy the platform publicly so more students can benefit from intelligent internship discovery.

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