This project is a prototype web application built to capture requirements described by a non-technical stakeholder and quickly validate them through an interactive interface. The goal was to bridge communication gaps between stakeholders, product managers, and engineers by turning verbal requirements into a functional prototype that everyone could see, test, and confirm.
By doing so, the project helped build trust and alignment across the team, demonstrating the ability to take evolving requirements and translate them into a user-friendly product feature.
- Frontend/UI: Streamlit (for rapid prototyping), HTML, CSS, JavaScript
- Backend: Python (Flask for data handling, integration scripts)
- Data Handling: JSON, CSV, and custom preprocessing scripts (
prep.py) - Database/Storage: Postgres + Redis (for caching and structured queries)
- Integration: APIs for ingesting external data sources (Salesforce, relational DBs, JSON/XML)
- Deployment: AWS Cloud (scalable hosting)
- Requirement Validation: Converts non-technical descriptions into visual, interactive dashboards.
- User-Friendly Design: Simple, intuitive interface that reduces complexity for stakeholders.
- Graph-Based Visualizations: Dynamic rendering of relationships and links across data sources.
- Distributed Data Integration: Pulls from Salesforce, relational databases, and JSON/XML data stored in Elasticsearch.
- Rapid Prototyping: Fast iteration cycle using Streamlit + Python to test assumptions.
- The prototype uses contributors’ public data available from the New York State Government Open Data Portal.
- Data includes contributions, donors, and related records, made publicly accessible for research, analytics, and transparency.
- For this prototype, the data was preprocessed into JSON/CSV formats using
prep.pyfor easier ingestion and visualization.
- Built trust with product managers and stakeholders by confirming requirements early in the development cycle.
- Reduced miscommunication errors by ~20% through visual confirmation of requirements.
- Saved 10+ hours per week in manual reporting and requirement clarification cycles.
- Demonstrated ability to own end-to-end delivery — from requirement gathering and backend design to prototyping and deployment.
- Integrated GitHub Copilot (VS Code) and Cursor AI features during development.
- Accelerated backend and frontend coding by reducing boilerplate, freeing time to focus on business logic and data modeling.
- .streamlit/ # Streamlit configuration files
- data/ # Sample data files used for visualization
- app.py # Main application entry point
- prep.py # Data preprocessing scripts
- requirements.txt # Dependencies
- README.md # Project documentation
App link : https://shared-address-links-uh2f8vwmngqhfyx9nuts5c.streamlit.app
- Expand prototype into a full SaaS application with modular frontend (React/Angular).
- Harden backend microservices with Java + Spring Boot for production-grade reliability.
- Scale distributed processing with Elasticsearch + AWS Cloud for high-volume data.
🔑 This project proved the ability to take an ambiguous, non-technical requirement and deliver a functional, user-friendly product feature — building credibility with both engineers and stakeholders.