Applied AI & Knowledge-Grounded Search

01 / OVERVIEW

Context & problem

Built AI-assisted financial-analysis and educational-content applications that retrieve source material and use it to ground generated outputs. Projects included founder-resource discovery and course-material improvement.

Generated content is more useful when users can trace it back to relevant source material. The applications explored retrieval as a bridge between unstructured knowledge and task-specific generation.

02 / SYSTEM ARCHITECTURE

How the pieces connect.

03 / INDIVIDUAL CONTRIBUTION

What I worked on.

• Built NLP and vector-search workflows for source discovery.
• Used BERT and FAISS-based retrieval with MongoDB-backed data.
• Applied retrieval to founder resources, financial insights, and educational content.

04 / IMPLEMENTATION

Engineering details.

Generated content is more useful when users can trace it back to relevant source material. The applications explored retrieval as a bridge between unstructured knowledge and task-specific generation.

• Content is represented for semantic retrieval.
• Retrieved source context informs downstream AI-assisted analysis or content improvement.

05 / RESULTS & VALIDATION

What the work supports.

• Delivered working application experiments across finance and education use cases.
• No unsupported user or business impact metric is claimed.

Validation details are limited to evidence stated in the resume; no unreported datasets, accuracy claims, or benchmark comparisons are added.