Hasan M.

If your AI feature makes things up instead of answering from your real data, or your app slows to a crawl the second traffic spikes, that's exactly what gets fixed here, with Next.js, Node.js, RAG and LLM integrations built for production. Most "full stack developers" will wire up a CRUD app and call it done. What you get here is different: AI connected to your actual business data through RAG and vector search, not a generic wrapper around a public model, shipped inside a production Next.js and Node app that holds up under real traffic instead of just demoing well. Here's what that's looked like in practice: An MDCAT exam prep platform serving 100,000+ students across Pakistan needed to survive brutal traffic spikes every exam season without going down. The fix: a 170,000+ question question bank, a quiz builder, a student dashboard and checkout for print books, built on Next.js, React, Node.js and MongoDB, hosted on Vercel with media served from DigitalOcean Spaces, so the platform holds even when the whole country logs in at once. An AI career platform needed every user to get their own chatbot that answers recruiter questions using only that person's resume and LinkedIn data, nothing invented. That meant building a RAG pipeline end to end: parsing documents, chunking them across 4 categories, roles, skills, education and projects, embedding each into a per user vector index, and tuning the retrieval prompt so it only answers from real data and says so honestly when it doesn't know. Update the profile and only that user's index rebuilds, nothing else on the platform touches it. An autonomous AI livestreaming platform needed to generate content, drive an interactive AI avatar and broadcast it live to X/Twitter with no human running the show. Built a distributed pipeline orchestrating 12+ tools, Next.js, Node.js, Python, Qdrant, OpenAI embeddings, HeyGen, Puppeteer, WebSockets, FFmpeg, AWS, Unreal Engine and OBS, running live across both Linux and Windows. Core stack: Next.js, React, Node.js, NestJS, PostgreSQL, MongoDB, Prisma, Docker, AWS, Python, vector databases (Qdrant), OpenAI embeddings and LLM APIs. Send over the app or the AI feature you're stuck on and you'll get an honest answer on whether RAG is even the right approach for it, free, before you spend a cent on a developer.