Put AI Agents to Work in Your Business
Identify where agentic AI creates measurable efficiency, pilot it safely, scale it with governance. Built by someone who runs multi-agent systems himself.
AI agents can now read documents, draft responses, move data between systems, and complete multi-step work while a person reviews the result. The hard part is choosing the right work to hand them and doing it safely.
I run multi-agent AI systems myself, on infrastructure I built and operate. That practice shapes my advice. I know where agents save real time and where they still need a person close by.
How an engagement works
- Opportunity assessment. I talk with the people doing the work and map where their time goes. You receive a short list of candidate workflows ranked by value, risk, and effort.
- Pilot. We choose one workflow, build or configure the agent, and measure it against the current process.
- Scale with governance. Pilots that prove their value move into production with access controls, logging, human review points, and a clear owner.
Where agents tend to help first
- Intake and triage of email, forms, and requests.
- Drafting routine documents from templates and prior work.
- Research and summarization across internal files.
- Moving data between systems that do not connect to each other.
Build or buy
Many needs can be met with tools you already license, such as Microsoft Copilot or Claude. When a workflow needs something custom, I design it and my delivery partner, Number Zoo, builds it. I tell you up front which path I recommend and why.
- Managing training and data retention across Claude plans
Training and retention are separate controls on every Claude plan. What each plan does by default, how to stop training, where zero data retention is actually available, and what I would configure for an individual, a small firm, and a law firm.