Yareta
A full-stack AI platform combining assessment workflows, user response handling, GraphQL APIs, and LLM-driven analysis to support structured insights around entrepreneurial potential.
AI-powered entrepreneurial potential assessment platform.
A full-stack AI platform combining assessment workflows, user response handling, GraphQL APIs, and LLM-driven analysis to support structured insights around entrepreneurial potential.
The product need
Founder and entrepreneurial potential assessment involves more than collecting form responses. The product needed to capture structured inputs, process them through backend services, and surface AI-assisted insights in a clear product experience.
The full-stack approach
I contributed to a responsive assessment platform with GraphQL-backed data flows and LangChain-powered LLM analysis, connecting user responses, backend services, and structured recommendations into one system.
What I worked on
- Worked on full-stack development for the AI-driven platform.
- Built responsive frontend modules using Next.js, TypeScript, Material UI, and Tailwind CSS.
- Connected assessment flows with GraphQL APIs.
- Managed assessment data, user responses, and AI-generated outputs.
- Integrated LLM workflows using LangChain for structured insights and recommendation logic.
Capabilities that mattered most
Assessment Engine UI
Built responsive interface modules for structured assessment flows, helping users move through complex inputs smoothly.
GraphQL Data Flow
Connected frontend modules with GraphQL APIs to manage assessment data, user responses, and AI-generated outputs.
LangChain Workflows
Integrated LLM workflows with LangChain to support intelligent analysis, structured insights, and recommendation logic.
Responsive Product Experience
Delivered a modern user experience with Next.js, TypeScript, Material UI, and Tailwind CSS.
Technical flow
Technology and service surface area
What this case study demonstrates
- Balancing structured assessment flows with a UX that still feels lightweight.
- Keeping AI-generated outputs grounded in predictable data and product states.
- Connecting frontend steps, API contracts, and analysis workflows without breaking continuity.
- Created a cleaner bridge between assessment interfaces and AI-backed insight generation.
- Strengthened the product’s full-stack architecture across UI, data flows, and intelligent workflows.
This project shows Ashish's ability to work on AI-enabled full-stack products where frontend UX, API contracts, user data, and LLM workflows need to work together as one experience.