Built and launched WebDoctor AI 🌐🧠

Jashraj shah — Wed, 27 May 2026 12:12:50 +0000

An AI-powered website intelligence platform that analyzes websites and turns diagnostics into an interactive neural command center experience.

Instead of showing traditional dashboards, I wanted to experiment with a more immersive approach using a real-time 3D interface and AI-driven analysis.

Features:

⚡ Website performance analysis
🛡 Security inspection
🔍 SEO diagnostics
🌍 Domain intelligence + WHOIS analysis
🧠 AI-generated recommendations
📊 Historical scan tracking
🌌 Interactive Three.js neural visualization

Tech stack:

Frontend:
• Next.js
• React Three Fiber / Three.js
• TailwindCSS

Backend:
• FastAPI
• Python

Database + AI:
• Supabase
• Groq AI

Deployment:
• Vercel
• Railway

One interesting part of building this was solving real-world deployment and system issues:

→ Railway 502 debugging
→ Backend runtime failures
→ Frontend–backend connection bugs
→ API dependency conflicts
→ Environment configuration issues
→ Production deployment troubleshooting

Live Demo:

🌐 https://web-doctor-ai.vercel.app/

Github:

cybertarr-A / WebDoctor-AI

WebDoctor AI is an enterprise-grade SaaS platform that acts as an "intelligent doctor" for websites. Enter any URL to compile real-time diagnostic reports, evaluate transport security, map technical frameworks, calculate standardized metrics, and generate deep, actionable AI-powered recommendations.

🌐 WebDoctor AI — Neural Website Intelligence Platform

Intelligent Website Diagnostics & AI-Powered Audit Platform

Analyze websites using AI-driven diagnostics, security analysis, SEO intelligence, performance scanning, and immersive neural visualization.

🚀 Live Demo: https://web-doctor-ai.vercel.app/

📖 Overview

WebDoctor AI is a full-stack AI-powered diagnostic platform designed to analyze websites and generate intelligent insights through a futuristic neural command center interface.

The system combines:

AI-driven recommendations
Website performance analysis
SEO diagnostics
Security inspection
Domain intelligence
Website DNA analysis
Historical report tracking
Interactive 3D visualization

✨ Features

🧠 AI Intelligence Engine

AI-generated recommendations
Heuristic analysis fallback
Website behavior profiling
Automated issue prioritization

🔍 Website Diagnostics

Performance analysis
Security checks
SEO auditing
Accessibility analysis
Robots.txt detection
Sitemap detection
Domain analysis
WHOIS lookup

🌌 Neural Command Center

Real-time Three.js visualization
Interactive orbital modules
Dynamic AI core rendering
Camera focus transitions
Particle-driven WebGL environment
Cyberpunk neural UI

📊 Historical Analytics

Previous scan tracking
Report retrieval
Executive…

View on GitHub

Would appreciate feedback from developers and builders — especially around UI experience, diagnostics quality, and ideas for future features.

How I Built an AI System That Actually Thinks in Pipelines (Not Just Prompts)

Jashraj shah — Sun, 03 May 2026 07:27:14 +0000

Most AI apps today are just prompt wrappers.

I wanted to build something different — a system that thinks in pipelines, not single responses.

So I designed an architecture with:

Input processing → Context enrichment
Vector search (FAISS) → Memory recall
LLM reasoning → Decision layer
Output refinement → Structured response

The key idea:
AI shouldn’t just answer — it should process information like a system.

Instead of:
User → Prompt → Output

I built:
User → Multi-stage pipeline → Intelligent result

This approach improves:

Accuracy
Context awareness
Scalability

Stack:
Python + FastAPI + Groq + FAISS + Supabase

Next step:
Turning this into a modular agent system where each component becomes a specialized AI.

Curious — are you building AI apps as tools or as systems?

DEV Community: Jashraj shah