AI Automation Build
LLM workflows in real operations, with a human approval gate
I build AI automation that runs in production, not in a demo: approval-gated agents, document-to-table pipelines, and natural-language-to-SQL with guardrails. $10–25k per bounded process, plus a monthly retainer to run and improve it.
The problem
You want to put LLMs to work in real operations, but you cannot hand an agent the keys and hope it never takes an irreversible action.
The hard part of AI automation is not the model call, it is control. I build workflows where every action that matters routes to a human approval gate first, every step is logged and traceable, and the model runs against your data with guardrails instead of free rein. Document-to-table extraction, natural-language querying over your warehouse, agents that investigate and propose but wait for a yes.
The living demo is AEGIS, my own automation platform. Its first version is open source and archived on GitHub: 3 agents running 49 workflow types over 72 permission-gated tools, with every human handoff a single reviewable record. You can read exactly how the gates work before you commit to anything.