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Contact · Open to architecture, AI platform and backend engagements

Bring me the messy version.

Half-formed ideas, systems that buckle under load, AI features that need to be cheaper or safer. That's my favourite starting point.

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Based in: Surat, India
Local time: IST (UTC+5:30)
Elsewhere: LinkedIn ↗GitHub ↗

01 · What are we building?

Architecture reviewAI / RAG platformBackend & event-driven systemsSecurity & SSOLead a buildSomething else

02 · Timing

ExploringThis quarterSoon as possible

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Nischay Gupta

All work07 / 07

Real-Time Consultation Apps

Two live Play Store apps with chat and voice/video calling over WebRTC, scaled to 10,000+ concurrent users.

Role: Lead Developer
When: 2025
Where: Shamonk Pvt. Ltd.
Client: Shamonk

concurrent users
apps live on Google Play

01The problem
02The system, drawn
03Build
04Stack

The problem

The problem

Users and expert agents needed low-latency chat and calls on mobile.

The system, drawn

The system, drawn

Fig. 03Signalling, then peer-to-peer: how a call starts in the user and agent appsSimplified redrawing · illustrative data · real mechanism

How it works · 1 part

01
Build
Two React Native apps (User and Agent) with real-time chat and voice/video over WebRTC on custom signalling infrastructure, backed by Spring Boot services.

Stack

React Native
WebRTC
Socket.IO
Java Spring Boot
PostgreSQL
MongoDB
Redis
AWS

User app on Google Play ↗Agent app on Google Play ↗

Go deeper

Want the full walkthrough?

I’ll take you through how Real-Time Consultation Apps is put together: the architecture, the trade-offs and the parts that were hard, then sketch how the same approach would fit your system. Thirty minutes, a whiteboard, no slides.

Talk through a solution Replies within two working days · IST

Next projectField Feedback Intelligence & RAG Analytics PlatformTurns code-mixed voice notes from the field into governed, queryable business intelligence — with every model running on local GPU.Keep reading