AI Agent | MCP & RAG Integration | Production Deployment

You don't need another AI demo that looks great in a sandbox and breaks the moment real users touch it. You need agents that run in production, connect to your tools via MCP and APIs, and keep working without you babysitting them.

I typically work with people in one of three situations:
- A manual, repetitive process is eating hours every week that an AI agent could realistically handle end-to-end
- An AI prototype that never made it past "it works on my machine"
- You need an LLM properly wired into your existing product or workflow, not bolted on as an afterthought

What I actually build:
- AI agents that use tools, retrieve real data, and complete multi-step tasks (not single-prompt chatbots)
- RAG systems built on your own data, with retrieval and answer quality actually measured, not assumed
- MCP servers that connect your tools, data, and internal systems to AI agents — yours or any other AI client — instead of a one-off script that breaks the moment something changes
- Integration into your existing stack: APIs, databases, internal tools
- Containerized, deployed, and monitored, so it's still working correctly a month later, not just in the demo

If your AI project needs to work in production, not just work once, let's talk about what you're building.