// How it works
Diagnostic-first. No guesswork.
I don't lead with a fixed package. Most work starts small and grows only if it makes sense — so you get clarity before you commit budget.
// Engagement phases
Discovery
An initial conversation to understand your platform and data state, your AI goals, and where reliability risk actually lives. No cost, no gatekeeping.
Assessment
A scoped AI Readiness Audit or technical deep-dive. You get a clear, honest read on readiness, gaps, and the smallest set of changes that move the needle.
Engagement
Fractional or contract work, or a larger initiative — reliability and platform engineering delivered with SLOs, observability, and repeatable infrastructure.
Handoff
Knowledge transfer so your team owns what we built, with ongoing support options if you want a reliability backstop as things evolve.
// Who this works for
Good fit
- Financial services or regulated enterprise (banks, insurers, mid-market manufacturers)
- An AI initiative in planning or early production that needs reliability architecture
- Platform engineering that has grown faster than the practices that keep it stable
- A VP/Director of Engineering who needs senior judgment without a full-time hire
- Procurement teams that need C2C, W-2, or direct engagement structures
Not a fit
- Pure MVP / prototype work where reliability is not yet a constraint
- Teams that want a vendor to manage a full development organization
- Projects with no engineering team — I work alongside teams, not instead of them
- Consumer apps without infrastructure complexity
// Common questions
Ready to start the diagnostic?
The first conversation is the lowest-risk, highest-clarity thing you can do. Let's have it.