Most startups do not need more demos. They need a clean architecture for what the agent should do, what it should remember, how it gets evaluated, when a human takes over, and how the whole thing stays useful after launch. That is the work.
Best fit: founders and product teams already building with LLMs, copilots, or multi-agent workflows.
I help you decide whether this should be one agent, a workflow, or a human-in-the-loop system before you waste months on the wrong shape.
Short-term context, procedural memory, retrieval, and file-backed state need different jobs. Most teams blur them and pay for it later.
If you cannot inspect failures, replay runs, and measure regressions, your agent will slowly become expensive theater.
The system has to fit how your team actually works, not just what looked good in the demo. Handoffs, approvals, alerts, and cost ceilings matter.
I have been on the engineering side, founder side, and operator side. That matters because agent systems do not fail only at the model layer. They fail at architecture, workflow design, incentives, vague ownership, and bad handoffs between humans and software.
If you want the shortest path to something usable, start with a teardown. We look at the current stack, where it breaks, and what the smallest practical architecture should be.
Yes, if you already know the workflow you want to improve. Early is fine. Vague is not.
Yes. Strategy by itself is not enough. I can help shape the architecture, define the operating model, and work with the team on the implementation plan.
Start with a free AI stack teardown. If there is a real fit, we go deeper from there.
Send the product, workflow, or architecture doc ahead of time. I will come into the call already looking for the real failure points.