Pick one painful process. We rebuild it with AI in 2 to 4 weeks. You see real numbers before we're done.

We start with a working session. Together we list the processes in your business that are slowest, most manual, and most valuable. We stack rank by value and feasibility. We pick one for the sprint.

The right candidate is scoped (clear inputs, clear outputs, one team owns it), painful (someone in your business loses real hours every week), and repeatable (if it works here, the pattern likely extends to other processes).

I sit with the people who run the process today. I watch them do it. I document every step: inputs, decisions, tools used, outputs produced. I capture frustrations, edge cases, and the error rate that nobody likes to talk about.

From that, we design the new flow. We identify where AI compresses time, cost, or error. We decide what stays human (the parts that require judgment, accountability, or edge-case escalation). We pick the tooling, define the evaluation criteria, and agree on what "good output" looks like.

I build the v1. That can mean prompts, custom skills, internal tools, managed agents, or integrations with the systems your team already uses. Whatever the new process needs to actually run.

Alongside the v1, I pull together a set of real examples from your work: cases where your team already knows what a good answer looks like. We tune the v1 until it gets those right. Then we run it side by side with how your team does the work today, compare the outputs, and fix anything that's off.

We roll out to one team for real use. The new process runs for one to two weeks while we capture real-world metrics: time saved, cost saved, error rate, throughput. Not estimated savings. Actual ones.

At the end of the sprint we sit down for a wrap-up meeting. I present the before-and-after numbers in plain language for your leadership team. Three outcomes are possible:

• It worked. The sprint is done. If you want to run another sprint on a different process, we can. No obligation.
• It mostly worked. I tune it for a follow-on week or two at no additional cost. Then it's your call whether to do another sprint.
• It didn't work. We stop here. You only paid for one sprint, not an open-ended engagement.

Regardless of outcome, your team owns everything we built: the working system, the documentation, the test cases, the prompt templates. The playbook is yours.

What you have at the end

• A real AI-assisted version of one process, running in production on your data
• Numbers like "3 hours per case down to 18 minutes" that hold up in a board meeting
• An annual savings figure you can take to your CFO
• Documentation so your team can repeat the pattern themselves
• A clear read on whether to run another sprint or stop here

What this actually costs

Most engagements bury the real cost. Here it is up front. The sprint fee is only one part.

• Sprint fee. Fixed, quoted up front based on scope.
• Your team's time during the sprint. 10 to 20 hours of process-owner time across 2 to 4 weeks. Interviews, validation, final review.
• Ongoing AI tool costs. Claude, GPT, or similar API costs once the new process is live. Usually $50 to $500 per month depending on volume.
• Ongoing maintenance. 1 to 2 hours per month of your team's time to monitor outputs and handle edge cases. We design the process so this stays small.

No surprises later. We lay this out concretely.