PingGenius Backend

Transform LinkedIn profile information into personalized outreach sequences.

Built with FastAPI for simple, reliable use.

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The Story

In October 2025 I started building PingGenius as my first serious AI project.

I wanted to solve a real problem: most LinkedIn outreach tools either scrape data or generate generic messages that get ignored.

After months of work I learned what it takes to make an AI backend reliable:

• avoid incorrect output
• keep responses consistent
• control costs and timeouts
• make messages sound natural

I paused product work for a while to focus on my mental health, but I kept the backend alive because the engineering lessons were valuable.

This is a clean FastAPI backend. It can power a SaaS or be used as a foundation for your own outreach tool.

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Core Capabilities

• Smart profile analysis – extracts role, company, industry, pain points, and recent activity
• Personalized sequences – connection notes, DMs, and follow-ups
• Tone control – Friendly, Direct, Authority, Casual
• Message refinement – improve messages while keeping the sequence consistent
• Human-in-the-loop – you copy/paste; no auto-sending
• Pain anchoring – find the main pain point before generating

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Technical Features

• FastAPI (async)
• OpenAI Python SDK
• MongoDB
• Authentication and rate limiting
• Swagger/OpenAPI docs
• GDPR-compliant handling
• Timeouts, output sanitization, and idempotency

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Quick Start

Prerequisites

• Python 3.11+
• uv package manager (recommended)

Installation

git clone https://github.com/hasnainXdev/pinggenius_backend
cd pinggenius_backend

python -m venv venv
source venv/bin/activate    # Windows: venv\Scripts\activate

uv add -r requirements.txt

Run locally

uvicorn main:app --reload

Open http://localhost:8000/docs for Swagger UI.

API Documentation

All v1 endpoints are documented.

Key endpoints:

• POST /api/v1/profile/analyze – analyze LinkedIn profile data
• POST /api/v1/outreach/generate – generate outreach sequence
• POST /api/v1/outreach/refine – refine messages
• GET /api/v1/outreach/{id} – retrieve saved sequence

Full docs → /docs

Security & Compliance

• no account risk (copy-paste only)
• GDPR-compliant data handling
• rate limiting and request validation
• timeouts and output checks
• idempotency to avoid duplicate processing

What I Learned

This project taught me about building an AI backend:

• making outputs predictable
• why many AI tools fail
• why safety layers matter

The main issues were fixed before I considered this ready.

Development Roadmap

Must-fix (Completed)

• guard against empty/weak profiles
• deterministic output sanitization
• timeout and runaway protection
• idempotency
• pain anchoring
• sequence cohesion memory
• tone drift protection

Nice-to-have

• reply-probability scoring
• A/B variants
• LinkedIn policy-safe checker

Built By

Muhammad Hasnain

AI Engineer & Full-Stack Developer from Karachi, Pakistan hasnainXdev on GitHub & X

Building useful AI tools for peoples.

For developers: fork it and improve it.