I work directly with founders and teams to identify useful opportunities, redesign workflows, and implement systems that can move from experiment to everyday use. The work stays close to the operators who understand the process.
We map the existing workflow before deciding what to automate. The deliverable is a usable system and a clear handoff—not a generic AI strategy deck.
Find the few opportunities worth testing before committing to a large build.
Test one high-value workflow with real users, processes, and data.
Turn a promising pilot into a system the team can use and maintain.
Build around the process when off-the-shelf software creates more friction than it removes.
Map the current workflow, business context, constraints, and highest-value opportunities.
Build a narrow working test and evaluate it with the people who do the work.
Develop, integrate, document, train, and prepare the system for everyday use.
Review quality, cost, adoption, and performance, then refine what matters.
A good starting point is a real operating problem: repetitive work, a fragmented process, an AI experiment that needs to become reliable, or software that no longer fits the team.
I can lead the discovery and build, work alongside an internal owner, train the people using the system, and leave behind documentation that makes the implementation usable after launch.