I build control, verification, and automation layers around large language models so they can be used reliably in real systems.
I don’t treat AI as “intelligence.” I treat it as what it is: a probabilistic engine that requires structure, constraints, and oversight to be useful at scale.
My work focuses on designing orchestration pipelines that reduce hallucinations, enforce consistency, and make system behavior observable over time. This includes multi-agent workflows, validation layers, human-in-the-loop checkpoints, and deterministic fallbacks for high-risk operations.
Practically, I help teams replace fragile, manual workflows with auditable systems that:
– behave consistently under load
– fail predictably instead of silently
– expose clear provenance and decision paths
– reduce operational risk without retraining models
I’ve built end-to-end automation and middleware using tools like n8n,
Make.com, LangGraph, Python, vector databases, and API-driven services — always with an emphasis on reliability, maintainability, and post-deployment clarity.
I’m especially interested in AI systems where trust, governance, and error cost matter more than raw performance: internal tooling, enterprise workflows, compliance-sensitive environments, and infrastructure-level AI.
If you’re working on AI systems that need to behave, not just impress, I’m always open to thoughtful collaboration.
