Keep what works
If an existing platform is valuable, make it cleaner, faster, and easier to operate. Use what you already purchased well.
For operators, founders, and technical teams
I help teams get more from the systems they own and retire the parts that slow work down. Existing platforms can be cleaned up when they still earn their keep. When they become drag, the better move is usually one small purpose-built tool. AI belongs where it makes the job clearer, faster, or easier to trust.
If an existing platform is valuable, make it cleaner, faster, and easier to operate. Use what you already purchased well.
When a system is expensive, slow to change, and no longer matched to the team, replace the smallest useful part first.
Purpose-built tools can match the way people work instead of forcing people to work the way a platform wants.
Getting started
I use a simple process: map the workflow, separate what matters from what adds noise, build one useful slice, and iterate from real use with operators, founders, and technically forward teams.
Talk through the first sliceI use Claude while the problem is still fuzzy: ideating, pressure-testing assumptions, mapping options, and working through decisions before code exists. A plan only matters if it turns into something real.
I use Codex once the direction is clear: implementing, refactoring, writing tests, wiring commands, reviewing diffs, and pushing through details. It works best with a tight scope and a way to prove the change.
I use local tools for private exploration, sensitive workflows, offline review, and helping people learn what is possible without handing every task to a cloud service. Production paths still need reliability and traceability.
Separate useful infrastructure from inherited complexity, unused licenses, duplicate workflows, and avoidable integration cost.
Design and ship a smaller tool that solves the real job: intake, review, tracking, approvals, reporting, handoffs, or customer operations.
Use Claude for planning, Codex for implementation, OpenClaw and local AI for private exploration, and tight feedback cycles to improve the tool while the team is already using it.