Your AI demo impressed the seed investors. Now Series A is asking about production architecture, model evaluation frameworks, and scaling plans. I advise AI startups at Berkeley SkyDeck and built 10 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 — covering OT/IT boundary, sensor-data pipelines, edge-inference readiness, and safety-regime maturity — plus a 90-day roadmap you can defend to your board
Four weeks to a strategy document, a business case, an ROI model, and a 12-month execution plan — scoped for industrial operators where OEE, safety-regime timelines, and physical-system procurement cycles are the real constraints, not just board optics
Twelve weeks to a production-grade multi-agent system that serves as the software and control-plane complement to your cyber-physical stack — fleet intelligence, SCADA-adjacent orchestration, or autonomous operations — with the eval harness, the observability stack, and the SRE handoff your team needs to operate it
Twelve weeks to harden an edge or embedded AI pilot stuck before production — on constrained hardware, inside safety envelopes, under latency and reliability requirements the pilot was never designed to meet
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 — with special depth on Annex III high-risk categories for autonomous, industrial, and robotics deployments
Part-time executive AI leadership — six to twelve months, two days a week — owning the AI P&L, governing physical-AI risk, chairing the AI-safety board, aligning OT/IT AI policy with standards, and building the regulator-facing narrative for industrial, automotive, and energy organisations