Approach
AI as a force multiplier
I treat AI agents as force multipliers, not replacements for judgment. By leaning heavily on AI-assisted engineering over the past two years, I've cut time-to-market for production-capable apps and platforms to as little as 3–6 weeks. That range applies to a focused web or mobile product, or a data pipeline, built by a small team (one to a few engineers plus an agent fleet) working from a clear scope. It doesn't include upfront discovery on an undefined problem, or an integration built against a third party that hasn't shipped its side yet, since those timelines depend on someone other than me. For every engagement, I assemble a fleet of agents tuned to the work at hand: building and shipping web and mobile applications, engineering data pipelines, auditing infrastructure, and handling the detail work that used to take a full team.
Bridge CTO, not hands-off
Many CTO roles trend hands-off as they scale. I work as a bridge CTO instead, staying close to the code, deployments, real-time issues, and observability, not just the roadmap. Over more than 20 years I've paired that hands-on engineering with product and people management, taking ideas through to production and building maintenance plans that blend AI agents and human oversight, rather than handing off strategy and stepping back.
Real accountability
As your fractional CTO, I orchestrate that work: setting direction, reviewing output, and making the calls that carry real accountability. The result is technical delivery that moves at software speed instead of consulting-hours speed. That holds in heavily regulated environments too, where the accountability matters most.
Where this applies
This approach applies broadly, to technical due diligence, architecture reviews, and team build-out: anywhere that AI-assisted execution paired with experienced oversight can compress timelines without cutting corners.