viditparashar.mePricing

Rates & engagement terms

Rates are quoted per engagement after a short scoping call.

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  1. viditparashar.meviditparashar.me › projects › resup

    Resup.dev — a résumé builder aimed at the software that reads it first

    Case studyLive2026

    31 Mar 2026 — … parsers can read rather than what looks impressive, which is a genuinely constraining brief. I have no data on whether it improves interview rates. I can show that…

    • 2input formats accepted
    • parse → structure → optimise → exportpipeline stages
    • unknownmeasured effect on interview rate
    • Next.js
    • TypeScript
    • Python
    • Tailwind CSS
    • NLP
    • LLMs
  2. viditparashar.meviditparashar.me › projects › voxa

    Voxa — dictation that learns your words, and an assistant that can see…

    Case studyLive2026

    30 Jun 2026 — … first and then mangle every proper noun and piece of jargon you use. Assistants solve the second only if you paste in the context by hand, which costs more than it…

    • 95%+wake-word accuracy
    • 2personalisation surfaces
    • 0irreversible actions without confirmation
    • ElectronJS
    • Node.js
    • TypeScript
    • Microsoft Azure
    • Azure Functions
    • LLMs
    • +2 more
  3. viditparashar.meviditparashar.me › about › intro

    Vidit Parashar — full-stack engineer, applied LLM and voice systems

    I build AI features for domains where a confident wrong answer costs something. In practice that has meant clinical software — an ambient scribe, and agents for…

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Vidit Parashar

Full-stack and AI engineer

I build AI features for domains where a confident wrong answer costs something — mostly clinical software and voice agents.

Gurugram, India · IST (UTC+5:30) · viditsharma818@gmail.com · @viditparashar96 · in/vidit-parashar-056707225 · viditparashar.framer.website

Summary

I have spent the last two years putting large language models into production rather than into demos. Most of that has been healthcare — an ambient scribe that turns a consultation into an EHR-ready note, and a set of clinical agents deployed into university health systems — where the interesting problem is never the model, it is what the system does when it is unsure.

Experience

Selected projects

Technologies

AI and LLM systems
LLMs, LangChain, LLM orchestration, NLP pipelines, Model fine-tuning, Speech to text, Full-duplex voice, Agent orchestration, Workflow automation, Screen understanding
Languages
TypeScript, JavaScript, Python, SQL, HTML, CSS, Java
Frontend
React, Next.js, Tailwind CSS, Redux, Redux Toolkit, Zustand, ShadCN, Material UI, NextUI, Styled Components, Bootstrap, GSAP, ApexCharts, Recharts, PWA
Backend and APIs
Node.js, Microservices, Socket.io, Azure Functions, Prisma, Mongoose
Databases
PostgreSQL, MongoDB, NoSQL
Infrastructure and tooling
Microsoft Azure, AWS, Docker, CI/CD, Git, Vercel, Firebase, Appwrite, Vite, Webpack
Mobile and desktop
Swift, SwiftUI, iOS (native), ElectronJS, React Native, Ionic

Education

Availability

open to interesting problems.