Less AI theatre. More useful systems.
I help teams understand where AI genuinely belongs in their workflow, then design and implement automations people can actually use.
A practical bridge between "we should use AI" and a working process your team trusts.
Advice, design, implementation.
Workflow audit
Map the current process, identify manual bottlenecks, and separate automation opportunities from tasks that should stay human.
AI use-case selection
Prioritize ideas by value, feasibility, risk, data readiness, and how easy they are to adopt inside the business.
Automation design
Turn the selected opportunity into a clear system design: inputs, tools, prompts, logic, handoffs, review steps, and failure modes.
Prototype and build support
Create working MVPs or support implementation with the right stack, validation evidence, and documentation.
A structured way to move from conversation to implementation.
Understand the business context, current workflow, and where time or quality is leaking.
Score opportunities and define the safest, highest-value first automation.
Build a small working version with real inputs, clear outputs, and human review.
Operationalize the system with documentation, monitoring, and adoption support.
The same judgement call keeps happening again.
The best automation candidates are not vague AI experiments. They are repeatable flows with clear inputs, consistent decisions, and a human who knows what good output looks like.
- Summarizing client calls into structured follow-up actions
- Qualifying inbound leads against a scorecard
- Tracking investor or company conversations
- Turning research into reusable briefs
- Drafting first-pass proposals or audit reports
- Monitoring operational signals and escalating exceptions
Start with the workflow, not the tool.
If you are exploring AI automation, the first step is understanding what should be automated, what should remain human, and what a useful first version would look like.