Ankita Patil
AI Orchestration and Fullstack Performance
New to Contra
Ankita is building their profile!
ProSculpt | React Performance & Bundle Optimization
ProSculpt is a production SaaS platform where I worked on frontend architecture and performance optimization.
As the application grew, its React production build had reached 68 MB, resulting in slower deployments, larger uploads, and unnecessary code being shipped to users.
I investigated the production bundle using Webpack Bundle Analyzer and traced the problem beyond individual components to how dependencies were structured across the application.
Key improvements:
• Reduced the production build from 68 MB to 21 MB
• Reduced build time from 3mins to 50secs
• Identified root-level imports that were silently defeating route-based code splitting
• Removed Highcharts from the main bundle and isolated it to dashboard routes where it was actually needed
• Removed unused static assets that were increasing deployment size
• Improved dependency boundaries so heavy feature-specific libraries weren't shipped to every user
• Improved deployment performance and overall bundle architecture
One of the key findings was that route-level lazy loading wasn't enough. A globally imported charting dependency was still pulling Highcharts into the main bundle, meaning users who never opened a dashboard were downloading it anyway.
Moving that configuration into the relevant feature modules reduced the build further to 21 MB and restored the intended code-splitting boundaries.
This work changed the application from simply using lazy loading to having dependency boundaries that actually reflected its feature architecture.
Tech: React, JavaScript, Webpack, Webpack Bundle Analyzer, Highcharts, Day.js, Code Splitting, Lazy Loading
0
7
VibeFit: AI Powered Wardrobe Recommendation Engine
VibeFit is an AI-powered virtual wardrobe that eliminates manual clothing organization by automatically extracting structured garment information from images and enabling semantic outfit discovery through natural language. Instead of relying on manually assigned categories or filters, the platform combines multimodal language models, vector search, and agent orchestration to create a searchable digital wardrobe that understands clothing by meaning rather than keywords. The platform is built around two independent LangGraph workflows. The Ingestion Workflow converts uploaded clothing images into structured wardrobe assets, while the Retrieval Workflow generates grounded outfit recommendations exclusively from garments the user owns. This separation keeps ingestion and retrieval independent, easier to maintain, and individually extensible.
Tech: Next.js 14, TypeScript, LangGraph, OpenAI, Gemini, Supabase, PostgreSQL, pgvector, LangSmith, Tailwind CSS
0
16
WOW Workplaces | Employee Engagement Platform
Built the frontend experience for WOW Workplaces, a public-facing employee engagement and workplace recognition platform by Jombay.
I worked on translating complex product requirements into responsive, production-ready interfaces, including workplace listings, organization filtering, employee review experiences, methodology sections, award categories, and content-heavy responsive layouts.
The platform presents workplace ratings and employee experiences across multiple industries while supporting Jombay's broader WOW Workplace Awards program.
My work focused on building maintainable frontend components, handling data-driven UI states, integrating backend APIs, and ensuring the experience worked consistently across screen sizes.
Key work:
• Owned end-to-end feature development and product delivery
• Built responsive, reusable frontend components
• Implemented data-driven workplace and employee review interfaces
• Integrated frontend flows with backend APIs
• Built filtering and dynamic content experiences
• Handled complex responsive layouts across desktop and mobile
• Worked through production UI and browser-specific issues
Tech: React, JavaScript, HTML, CSS/SCSS, REST APIs
0
26