Your AI demo impressed the seed investors. Now Series A is asking about production architecture, model evaluation frameworks, and scaling plans. I've mentored 30+ AI startups at Berkeley SkyDeck and built 8 AI ventures from zero to production. I know what breaks at scale — because I've broken it.
Technical leadership for AI-native startups. Architecture that survives due diligence, scales past demo day, and doesn't need to be rewritten at Series B.
Two weeks. A written readiness report ranked by severity, plus a 90-day roadmap you can actually defend to your board
Four weeks to a strategy document, a business case, an ROI model, and a 12-month execution plan — reconciled end to end and defensible to the board, the CFO, and your head of engineering
Twelve weeks to a multi-agent production system that holds up under real traffic, with the eval harness, the observability stack, and the SRE handoff your team needs to operate it without me
Twelve weeks to harden a working AI pilot into a system that will survive its commercial stage gate — whether that is an enterprise launch, a public sector go-live, an SME rollout, or a Series A
Eight weeks to engineer prompt-injection defense, PII redaction, audit logs, and a real threat model into your AI pipeline — before a red team finds them missing
Twelve to twenty-four weeks to risk-classify your AI systems, complete the conformity assessment, produce the Annex IV technical documentation, and stand up post-market monitoring — before an enforcement deadline finds you unprepared
A monthly retainer that runs your production AI like a production system — dashboards, alerts, eval regressions, on-call response, cost tracking, and the quarterly model-refresh call your team would otherwise skip
Part-time executive AI leadership — six to twelve months, two days a week — owning the AI P&L, governing risk, reporting to the board, and coaching the team you will eventually promote into the seat
Ship 10× like a 1000-person company with a 10-person team.