Problems I help solve
Why AI projects fail before production
Most AI projects fail for the same handful of reasons. I've seen them across industries and company sizes — and I know how to address them before they become blockers.
Systems that stall before production
A team ships a demo that impresses stakeholders, then spends months stuck in security review. The root cause is almost always the same: access controls, audit trails, and observability weren't designed in from the start.
How I address it
I start with an architecture memo that maps identity flows, data contracts, and failure modes before the first sprint. Security review becomes a formality, not a blocker.
Retrieval quality that degrades invisibly
Search pipelines return worse answers as source data shifts. Without evaluation harnesses and benchmarks, no one notices until users lose trust in the system.
How I address it
I build evaluation pipelines that score retrieval quality against fixed benchmarks and alert when results drift — so degradation is caught before it reaches users.
No audit trail when compliance asks questions
Healthcare, finance, and legal teams need to prove which data informed each AI response and who had access to it. Ad-hoc logging doesn't survive a compliance review.
How I address it
I instrument structured audit events at every decision point — tied to user identity and access policy at the time of the request.
AI costs that spiral without warning
Shared API credentials and unconstrained automation loops create both security exposure and cloud spend that compounds month-over-month.
How I address it
I scope credentials to least-privilege identities, set per-workflow cost budgets, and wire alerting into the observability layer before launch.
Operations teams flying blind
The demo works. Production doesn't. On-call engineers have no traces, no cost dashboards, and no runbooks — just a wall of unstructured logs.
How I address it
I treat observability as a first-class deliverable: distributed traces, cost dashboards tied to business metrics, and incident runbooks written before go-live.
Projects stuck mid-delivery
Six months in, teams discover the architecture won't scale, the access model doesn't fit their requirements, or stakeholder confidence has collapsed.
How I address it
I do a structured architecture review, identify what's salvageable, and produce an incremental reset plan with hands-on help to execute it.
Ready to talk?
If you're dealing with a specific problem — or you're not sure yet whether what you're building is going to hold up — I'm happy to spend 30 minutes on a call to find out.
No sales process. Just a conversation.