Low-Code/No-Code Development Projects in Lahore DHALow-Code/No-Code Development Projects in Lahore DHA
Projects
People
Cover image for The average household wastes around 132kg of food annually. ...
The average household wastes around 132kg of food annually. This is mostly caused by simply not knowing what to do with extra ingredients that are about to expire. So, I built a solution for that. Introducing Kitchen Scrap Alchemist, an AI-powered app that transmutes your leftovers into delightful meals in just a few seconds! You can generate recipes based on your inventory and save them to your cookbook for later. Ready to save food (and money)? Check it out here ✨ : https://devjuni.retool.com/p/kitchen-scrap-alchemist/homepage The Build Story: I had zero experience with tools like this before. But thanks to @Retool, I was able to build and deploy this entire app in just one night during a busy week. It really shows how powerful low-code tools are for beginners.
4
8
726
Cover image for Project Title: Engineering an Automated
Project Title: Engineering an Automated Intelligence Pipeline (RAG Workflow) The Challenge: In a world drowning in data, the problem isn't having information; it's accessing it instantly. Most businesses leave their most valuable assets buried in Google Drive folders where they remain static and underutilized. As a CS student and designer, I saw an opportunity to bridge the gap between "stored data" and "active intelligence." I wanted to build a system that doesn't just store files but understands them. The Solution: I engineered a sophisticated Retrieval-Augmented Generation (RAG) pipeline using n8n for orchestration. This isn't just a simple link-up; it’s a multi-layered architectural approach to data automation: Data Ingestion: Automatically triggering on Google Drive updates to ensure the AI's "brain" is always current. Vector Processing: Implementing a Recursive Character Text Splitter to ensure data chunks maintain semantic meaning. Memory & Storage: Utilizing Pinecone Vector Store for high-speed similarity searches, allowing for near-instant retrieval of relevant context. Model Integration: Leveraging the power of Google Gemini as the reasoning engine, supported by OpenAI Embeddings for high-precision vectorization. The Intersection of Design & Code: Being a 4th-semester BSCS student, I focused heavily on the logic of the workflow—ensuring the recursive splitting didn't lose context and that the vector database was optimized for performance. However, my background as a Graphic Designer allows me to visualize these complex backend processes into a user experience that feels intuitive. I believe that an automation is only as good as its usability; if the interface is clunky, the power of the AI is lost. Why This Matters for Your Business: This workflow effectively creates a "Custom Brain" for your organization. Imagine chatting with your company’s entire history, SOPs, and technical documents as if you were talking to an expert teammate. By automating this pipeline, I eliminate the need for manual data entry or tedious searching, allowing teams to focus on high-level creative work while the AI handles the information retrieval. Technical Stack Used: Logic: n8n Workflow Automation LLMs: Google Gemini & OpenAI Database: Pinecone (Vector Search) Storage: Google Drive API Theory: RAG (Retrieval-Augmented Generation) & Semantic Search
1
27