AI-Powered Lead Generation & Outreach Automation Platform

AI-Powered Lead Generation & Outreach Automation PlatformAI-Powered Lead Generation & Outreach Automation Platform

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Tashfeen Siddiq

pro

• Mar 11

AI Lead Generation & Outreach Automation Platform

Designed and developed a full-stack automation platform that helps businesses automatically generate leads, enrich contact data, and run personalized outreach campaigns at scale.

The system integrates multiple APIs, AI models, and CRM pipelines to automate the entire lead acquisition workflow from prospect discovery to outreach tracking.

The platform significantly reduces manual sales effort while increasing response rates through AI-driven personalization.

AI Automation Business Workflow Automation Full Stack Development N8N Next.js Python

Bhavy Shekhaliya

pro

• Mar 9

You're wasting 3+ hours/week writing social posts manually. 🕐

Here's what founders are doing instead:

One n8n workflow that researches your topic → writes LinkedIn, Reddit & X posts → matches your exact tone — sounds 100% human, fully automated.

How it works: → You pick a topic → AI researches it live → Generates platform-specific posts in your voice → Schedules & publishes automatically

No ghostwriter. No agency. No burnout.

E-commerce founders, solopreneurs & marketers are reclaiming their week while staying consistently visible across every platform.

Automation isn't the future it's what your competitors are already using.

Want to set this up for your business?

N8N Business Workflow Automation AI Automation

Kelis Shekhaliya

• Mar 9

Sound Junior Social Media Manager

Emmanuel Aderemi

pro

• Mar 8

Check it out. Built an automated lead-gen system for a cyber firm, capturing high-ticket UK gov leads and scaling the brand from invisible to authoritative.

Go High Level N8N OpenAI

Abhiram Tinguria

• Mar 8

Impressive build. Turning invisible brands into authority through automation is powerful. Curious — which part of the workflow in N8N handled the lead qualification?

Mani Sharan Bommakanti

• Mar 8

Built an end-to-end NLP pipeline to classify text reviews as Positive, Negative, or Neutral. Used NLTK for tokenization and lemmatization, TF-IDF for vectorization, and trained 3 ML classifiers — Logistic Regression, Naive Bayes, and SVM. Visualized sentiment trends and model accuracy using Matplotlib and Seaborn.