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Rashid Khabeer's avatar
Rashid Khabeer
Multan, Pakistan
Full-Stack Agentic AI Dev | n8n • Make • GHL Automation
New to Contra
Full-Stack Agentic AI Dev | n8n • Make • GHL Automation
Cover image for Is your HR inbox a
Is your HR inbox a time-consuming "black hole" 🕳️? I watched an HR team struggle to stay afloat while buried under a mountain of PTO requests, payroll glitches, and login issues. The "human" was being taken out of Human Resources by endless manual triage. So, I built an AI Orchestrator to give them their time back. Using n8n as the nervous system and GPT-4.1 as the brain, I created a workflow that: 1- Categorizes Intent: Instantly identifies if a message is a simple question or a sensitive payroll crisis. 2-Verifies Identity: Cross-checks Airtable to validate employee records in milliseconds. 3- Routes & Resolves: Seamlessly pushes data to iSolved, triggers specialized sub-workflows, or escalates complex cases to Zendesk. The Bottom Line: No more manual sorting. No more "lost" emails. Just instant, accurate routing that lets HR focus on supporting people, not managing tickets. 🚀 What’s one manual process in your business that feels like a "black hole" for time? 🕳️ #AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) #Automation (https://www.linkedin.com/search/results/all/?keywords=%23automation&origin=HASH_TAG_FROM_FEED) #FutureOfWork (https://www.linkedin.com/search/results/all/?keywords=%23futureofwork&origin=HASH_TAG_FROM_FEED) #HRTech (https://www.linkedin.com/search/results/all/?keywords=%23hrtech&origin=HASH_TAG_FROM_FEED) #n8n (https://www.linkedin.com/search/results/all/?keywords=%23n8n&origin=HASH_TAG_FROM_FEED) #Efficiency (https://www.linkedin.com/search/results/all/?keywords=%23efficiency&origin=HASH_TAG_FROM_FEED)
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Cover image for The MCAT King of the
The MCAT King of the Curve project is a sophisticated RAG-based (Retrieval-Augmented Generation) backend designed to revolutionize how medical students interact with vast amounts of unstructured study data. Built using Python and OpenAI, the system addresses the challenge of static, overwhelming notes by allowing users to upload diverse file formats—including PDFs, Excel, and images—and transform them into a searchable, interactive knowledge base. By utilizing ChromaDB for vector embeddings and semantic search, the platform provides highly accurate, source-grounded answers in under three seconds, effectively eliminating AI hallucinations. Beyond simple Q&A, the backend features a specialized "Classroom Mode" that auto-generates MCAT-style questions across varying difficulty levels, offering a gamified, scalable, and secure environment that prioritizes data isolation and high-performance reliability for high-stakes exam preparation.
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Cover image for Developed a comprehensive, end-to-end automation
Developed a comprehensive, end-to-end automation engine using n8n to replace slow, error-prone manual profit margin calculations with real-time, data-driven visibility. The system synchronizes order data across ShipStation, BigCommerce, and major marketplaces like Amazon and Walmart, integrating complex logic for item-level costs, multi-currency conversions (via Frankfurter API), and customer-specific discounts within Airtable. This automation reduced processing time to seconds per order and eliminated manual entry errors. Key strategic features include a low-margin detection system with an integrated team review workflow, automated management reporting, and a robust architecture handling deduplication and missing data fallbacks. The solution provides immediate, reliable profitability insights for pricing and operational decision-making.
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Cover image for Built JuvaBun, an AI-powered sales
Built JuvaBun, an AI-powered sales chatbot for a hair extensions e-commerce site, designed to automate customer conversations and drive conversions. The bot answers product questions, asks guided follow-ups, and recommends products based on hair type, budget, and user intent. Integrated rich product content including images, videos, reviews, and comparisons to reduce buyer hesitation. Implemented checkout link sharing, abandoned-chat follow-ups, and upsell flows for higher-value bundles. Designed the system with intent detection, fallback handling, and conversation recovery to avoid dead ends, improving engagement while reducing manual sales effort.
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