Live web discovery
Finds fresh sources on demand instead of relying only on static training data.
AI research system by Purohit Maitrayee
AI Research Agent is a full-stack research workflow that combines live web discovery, document extraction, vector embeddings, semantic retrieval, and citation-grounded synthesis in a single polished experience.
Designed and built by Purohit Maitrayee
Research Snapshot
What it demonstrates
Finds fresh sources on demand instead of relying only on static training data.
Fetches and extracts readable article text from webpages before indexing.
Uses embeddings and a vector store to rank the most relevant evidence chunks.
Produces a structured report that keeps sources visible and easy to verify.
Architecture
Collect relevant links using a focused web search tool.
Download pages and isolate readable article content.
Chunk source text and convert it into semantic vectors.
Match the query against the vector store to surface evidence.
Present the strongest findings in a concise cited report.
Interactive demo
Enter a query and follow the motion of the pipeline from discovery to synthesis. The interface is designed to communicate clarity, structure, and technical depth at a glance.
Project creator
Designed and developed by Purohit Maitrayee with a focus on research systems, retrieval quality, structured outputs, and thoughtful interface design.
Made by
I build AI systems focused on research workflows, strong retrieval, and clean, well-designed products.
Contact
For collaborations, opportunities, or project conversations, reach out directly. This website presents the system, the workflow, and the design thinking behind the project.