Rab N.

I build AI systems and automations that do two things: save you hours and make your app actually work with your data. The manual, repetitive backend work that slows your team down? I make it run on its own. The LLM everyone's talking about? I wire it into your actual product so it gives grounded, useful answers instead of guessing. The goal is always the same: cut manual work, connect AI to real data, and build systems that hold up in production, not demos that fall apart the first week. WHAT I USUALLY HELP WITH RAG systems that ground LLM answers in your own data instead of letting it hallucinate AI agents and automation workflows that chain tools, APIs, and steps together Custom automations and API integrations in Python or Ruby on Rails, when the job needs real logic and not a no-code template Vector search and semantic retrieval using PGVector and vector databases Backend architecture for SaaS products, built to scale past MVP without a rewrite Fixing or scaling automations and integrations someone else built and walked away from MY SKILLS AI & Automation RAG (Retrieval-Augmented Generation) pipelines LLM integration (OpenAI, Anthropic/Claude APIs) AI agents and agentic workflows Prompt engineering and evaluation Vector search, embeddings, semantic retrieval Workflow automation and business process automation Development Python for AI pipelines and automation scripting Ruby on Rails for high-level application architecture and APIs JavaScript/Node.js React, Next.js REST and GraphQL APIs, webhooks Databases: MySQL, PostgreSQL, PGVector Full-stack background (the part that makes the AI actually useful) CRM/CMS systems and structured content architecture SaaS dashboards with real roles, data, and edge cases API-driven web and mobile applications Ionic (React/Angular) for cross-platform apps Most people doing AI automation work come from a pure automation background. I didn't. I spent years building production frontends and backend architecture for real SaaS products before this, so I think about how AI fits into a system that already has users, not just a standalone script. When I add RAG or an AI agent to your product, I'm thinking about what happens when the data changes, when the model is wrong, when the system needs to scale. I build things to last, clean and modular, not a pile of prompts that breaks the moment your data shifts. I work fast, keep scope clear, and a messy brief doesn't scare me. Figuring out what you actually need before writing code is part of the job. If you've got a workflow eating your team's time, an app that needs to actually understand your data, or an AI idea you're not sure how to ship, send me an invite. Let's talk. Rab