Currently building autonomous AI systems that actually survive production — not just the demo.

Most AI projects work beautifully in a controlled test and then quietly fall apart on real data, edge cases, and concurrency. I focus on the unglamorous engineering that closes that gap: data validation pipelines, boundary checks, error handling, and knowing exactly where deterministic logic should own the decision instead of the model.

Recent work includes agentic workflows built with Python and FastAPI, an LLM reasoning layer for the genuinely ambiguous decisions, and automation orchestrated through Make with MCP. My background is technical support and operations, which means I design for the failure case first — I've spent years watching systems break in ways their builders never anticipated.

If you have an AI pilot that impressed everyone in the demo and then stalled on the way to something dependable, that gap is exactly what I build for.

Open to Applied AI / AI automation projects.

Keep it up!

Stop letting manual follow-ups and leaky funnels stall your growth.

True lead generation isn't about chasing cold contact.

It's about building automated, zero-friction systems that capture and convert traffic effortlessly.

Optimize your pipeline, scale your outreach, and let infrastructure do the heavy lifting.

For high ticket specifically, zero friction can work against you at the wrong step. capture & routing should be instant but the moment a real buyer expects a human usually right after qualifying, that is where automation should hand off not close the loop entirely