AI workflows often grow organically. New tools get added, agents overlap, expensive models handle simple work, humans spend time reviewing low-value output, and nobody has a clear picture of what the entire system is actually doing.

I help make that workflow legible.

I map the process from input to accepted outcome, examine the tools and agents involved, identify friction and failure points, and evaluate where cost, human effort, reliability, security, or unnecessary complexity can be improved.

The goal is not to recommend more AI.

The goal is to determine what is actually working, what is not earning its place, and what should change.

AI and developer tools

Agent workflows

Model usage and routing

Context and handoffs

Human review steps

Rework and retries

Workflow bottlenecks

Capability overlap

Permissions and trust concerns

Available cost and usage data

Reliability and operational burden

Current-state workflow map

Tool and agent inventory

Bottleneck and overlap findings

Cost and usage observations where data is available

Reliability and trust-surface observations

KEEP / CHANGE / REMOVE / INVESTIGATE recommendations

Prioritized improvement plan

Executive summary

Technical evidence appendix

One clearly defined workflow

Access to the people who actually use or own it

Relevant documentation, screenshots, exports, or approved system information

Representative examples of recent work

Available usage or cost data where relevant

Production access is not required by default. Any active testing, changes, or elevated access would be separately scoped and authorized.

Contact for pricing