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Internship project

Infoseek

A full-stack enterprise RAG prototype built during my internship at Infosys, designed to replace traditional intranet search systems.

Next.jsPythonGCPAlloyDBBig Query

No Repo Link (Private Codebase)

Infoseek logo, a stylised wordmark reading Infoseek

About the project

Built independently during my internship at Infosys, Infoseek explores a future where employees can ask questions using natural language instead of manually searching across fragmented knowledgebases.

It ingests PDFs and websites, preserves document structure while chunking content, creates semantic embeddings, and retrieves the most relevant evidence to ground each AI-generated answer.

The prototype pairs a polished Next.js interface with a Python and Flask retrieval pipeline, local Qwen inference, role-aware access controls, and GCP-backed vector search—anambitious foundation for an enterprise knowledge layer that could reduce dependence on conventional web search.

Highlights

  • Prototype built from the ground up independantly.
  • Implements both Naive and HyDE RAG pipelines
  • Built with big data principles using a range of the most effective GCP services.

Project gallery

Infoseek landing page with a central AI-powered search interface
The main landing page for Infoseek, a centralized search bar.
Infoseek insights dashboard showing search and engagement analytics
Insights dashboard showing search and engagement analytics, helping executives understand how employees are using the system.
Infoseek vector-based topic visualisation
The bottom half of the insights dashboard, showing a 3D interactive semantic clustering visualization.
Infoseek search results with an AI-generated answer and supporting sources
Answers are grounded in the most relevant retrieved document excerpts.
Infoseek admin interface for uploading and indexing knowledge sources
Administrator dashboard where they can add PDFs and URLs to the searchable knowledge base.