A few months ago, a client came to me with a problem: his job description and talent analysis service was charging $800 per report and took 6-8 hours of manual GPT work to deliver. He wanted to automate the whole thing so he could scale to dozens of clients a month. You always have two choices in cases like this: 1. Build a hardcoded solution just for this client 2. Build something flexible, so you can add new reports or even sell it as SaaS. I always pick option two. So, I proposed building a form builder that could generate custom forms for any kind of report - using uploaded company docs, CVs, psychology tests, and more. One month later, using Lovable, Supabase, and n8n, I built a system where you can set up any input/output forms, fill them in, and get a perfectly formatted report in 40 minutes (instead of 6 hours). The result: scalable, predictable, and always in the exact format the client needs, no matter what documents get uploaded. Now, that client can run 30–50 of these services a month with just one manager, pulling $30k+ MRR. I got $5k for the build (should have charged more, but still happy). ➡️ Want to learn how to build scalable AI apps like this for your business or own clients? I spent a week and prepared for you a guide on the most interesting topics about developing with Lovable - get it here.

A couple of weeks ago, @Raquaza Moss came to me with an request: “Is it possible to build a TikTok analyzer for viral videos?” (the answer is Yes) Also, he mentioned Poppy AI, which just hit $100K MRR last month, and called it a mind-blowing tool. I said, “I can build that in a few hours.” He replied: “No way.” Well… challenge accepted. In just 3 hours, I built a working prototype using Lovable, N8N, and Supabase. What It Does: ✅ Upload your own data – add text, YouTube videos (which get transcribed), and other sources. ✅ AI-powered content generation – AI learns from your sources, helping you create content in your own style. ✅ Multiple use cases: • Stay consistent by using your own videos as a knowledge base. • Copy viral video structures from competitors to increase engagement. It's easy to extend with other data sources such as reels and tiktoks, and templates for different types of content. 📌 Would you like to see a step-by-step video guide on how I built this? Drop a comment! 🚀

Many businesses are struggling to build truly smart RAG agents that can efficiently process large knowledge bases with hundreds (or even thousands) of files. The basic vector search approach is too weak—it often retrieves irrelevant chunks, lacks deeper context, and struggles with large datasets. To improve accuracy, additional steps are needed to enrich the context before answering user queries. So I started researching ways to enhance RAG systems and whether these strategies could be implemented in N8N. One of my initial ideas was pre-filtering the data before vector search. Instead of blindly searching across the entire database, we could first categorize files, add metadata links, and apply tag-based filtering to narrow down the search scope before retrieving relevant vector chunks. During my research, I came across a presentation from a LlamaIndex employee (they specialize in RAG for large enterprises) and discovered three practical techniques for improving RAG performance: 1️⃣ Context Expansion Instead of pulling only the most relevant vector, the system can retrieve adjacent vectors (e.g., 2 before and 2 after) to preserve context rather than extracting isolated fragments. ✅ Extra Idea: AI validation can be used to check if the expanded context is useful—if not, it can refine the search and output only the truly relevant chunk. 2️⃣ From Small to Big: Layered Search Instead of running vector search on raw chunks, add metadata summaries, tags, and categories to chunk metadata or a separate table. Then: 🔹 Step 1: Run a vector search only on the metadata layer to identify the most relevant sections. 🔹 Step 2: Retrieve the actual data only from these filtered sections. 📌 This approach requires well-structured metadata summarization based on common user queries and niche specifics. For example, in legal cases, metadata might include facts, dates, names, and key arguments. 3️⃣ Multi-Agent System Instead of retrieving a static dataset, the system can break down the query into multiple sub-questions and distribute them across different AI agents (or loop them iteratively).

⚠️ Link to guide and templates If you’re a freelancer working with AI or automation, you know how crucial PDFs are for many businesses. But extracting data from unstructured PDFs? That’s a pain—until now. With LlamaParse, you can build smarter automation flows that make handling complex PDFs simple, efficient, and highly valuable to your clients. Why You Should Care • Next-Level Parsing: LlamaParse is far superior to the default parse node in N8N, handling even the most complex, unstructured PDF documents. • Client-Ready Automation: Working with invoices, contracts, or reports? This guide will help you create a robust PDF parsing flow that your clients will love—and pay for. • Win More Projects: Offer tailored, scalable solutions that save your clients hours of manual work and position you as the go-to automation expert. What You’ll Learn 1️⃣ How to set up LlamaParse in N8N for powerful PDF parsing. 2️⃣ Extract and structure data from even the messiest PDFs with ease. 3️⃣ Build a complete automation flow to simplify your clients’ processes, including data organization and storage in Airtable. 🎥 Check out the video tutorial for a step-by-step walkthrough and start building smarter solutions for your clients today!

We're thrilled you're here! This community is the go-to place for everything related to AI agents, automations, and building powerful tools for your business or clients. ___ 🚀 Get Started Here: ➡️ Classroom (Templates & Guides) Start by exploring our Classroom—packed with video tutorials, templates, and detailed guides to help you get your AI automation up and running fast. ➡️ Your First AI Agent: We recommend beginning with our short course on building your first AI agent. It's an ideal introduction to quickly dive into the exciting world of AI automation. ___ 🌟 Introduce Yourself! Let's connect! Drop a quick introduction in the comments below—share who you are, your interests, and what you're aiming to achieve. Take a moment to browse through introductions from other community members and make connections with those who share similar goals. ___ 📈 Next Steps: Once you've completed your first agent, we encourage you to explore our advanced content, available exclusively to our paid community members: - N8N Automation Essentials - Advanced AI Agent Development - Building Interfaces for Your AI Automations - Creating SaaS Solutions for Your Business or Clients ___ 🎉 Excited to have you onboard! Let's build something amazing together!