Building a Remote Technical Interview Platform with AI Proctoring (Next.js + WASM)

Keshav Jangir — Fri, 26 Dec 2025 15:02:50 +0000

Remote technical interviews are deceptively hard.

It’s not just about video calls and a code editor — it’s about fairness, integrity, real-time sync, and low-latency feedback, all inside the browser.

Over the past few weeks, I’ve been building AssessIQ, a remote technical interview platform designed to solve these problems using modern web technologies.

This post breaks down what I built, the hard problems I faced, and the architectural decisions behind them.

The Core Problem

Traditional remote interviews suffer from:
Tab switching and external help
Lack of real-time visibility for interviewers
High-latency server-side proctoring
Poor synchronization between interviewer and candidate

I wanted to answer one question:

Can we enforce interview integrity without harming performance or privacy?

High-Level Architecture

AssessIQ is built around real-time, client-first design:

Tech Stack

Next.js + TypeScript – Application framework
Convex – Real-time backend & synchronized state
Clerk – Authentication & role-based access (Interviewer / Candidate)
Stream API – Video calls & interview events
Monaco Editor – In-browser code editor
MediaPipe FaceMesh (WebAssembly) – Client-side AI proctoring
Piston API (in progress) – Sandboxed code execution

Real-Time Interview Flow

Interviewer creates a session
Candidate joins with a restricted environment
Code, alerts, and interview state sync in real time
Integrity checks run continuously in the browser

Convex handles reactive state updates, ensuring both sides see changes instantly without manual polling.

Client-Side AI Proctoring with WebAssembly
One of the most critical design decisions was where proctoring should run.

Instead of sending video streams to a server, I used MediaPipe FaceMesh compiled to WebAssembly (WASM).

Why WASM?

Near-native performance inside the browser
No server inference latency
Privacy-friendly (video never leaves the device)
Scales without GPU servers

What it detects

Face presence
Gaze deviation
Camera disengagement

All detection logic runs locally, with only events (not video) propagated to the system.

Enforcing Interview Integrity

To prevent common cheating vectors, I implemented:

Fullscreen API enforcement
Tab visibility & focus tracking
Stream event hooks for automated warnings
Progressive escalation (warnings → flags)

The challenge wasn’t detecting violations — it was avoiding false positives and ensuring the UX stayed fair.

What’s Next

Finish Piston-based execution pipeline
Add integrity scoring per session
Improve false-positive mitigation
Harden rate limiting & audit logs

AssessIQ taught me that:

Frontend engineering is no longer “just UI”
Browsers are capable of serious system-level work
WebAssembly unlocks entirely new classes of applications
Real-time systems demand discipline, not shortcuts

If you’re building interview platforms, proctoring tools, or real-time web apps — I’d love to exchange ideas.

DEV Community: Keshav Jangir

Building a Remote Technical Interview Platform with AI Proctoring (Next.js + WASM)