Hands-on AI product engineer for established product teams
Ship the AI featureyour customers will actually use.
I help growing product companies identify the right AI opportunity, build it inside their existing product, and carry it to production, without hiring an AI team or handing the work to an agency.
Fractional CTO judgment. Senior engineer execution. One accountable owner.
Selected Case Studies
Shipped work with the numbers behind it: AI features in production, a platform serving 250K+ monthly users, and delivery that cut costs and timelines.
Dental Marketing Platform
Energize Group was running 200+ dental practice websites on fragmented infrastructure, where every new site added cost and every update took too long. I architected SmartSites, a unified multi-tenant CMS and hosting platform, and wired LLM content workflows directly into production operations. The result: infrastructure costs down 75%, deployment time down 60%, 250K+ monthly users served, and AI-assisted content shipping for 50+ blogs and sites every month.
The people who have shipped with me.
We handed Dante an ambiguous AI product problem, and he built a production feature that became instrumental to the product. He owned everything end to end, made the hard architecture calls, and shipped something our users now rely on every day. It works beautifully, and he did it without needing to be managed. He operates like a founding engineer, not a contractor.
Across several products, Dante consistently turned business goals into shipped software, fast. He understands what the business needs before he writes a line of code, raises the bar for the engineers around him, and stays hands-on the whole way. He is the rare senior engineer who can lead and build at the same time.
Why good AI initiatives stall.
You know the AI feature you need. It usually stalls for one of three reasons, and none of them are about the idea.
The opportunity sits between product, engineering, and leadership, so nobody carries it from decision to launch.
Experiments work in isolation but lack the architecture, evaluation, security, and integration real users require.
The team is capable, but the roadmap is already full and this cannot wait in the backlog.
I step in as the senior owner who defines the path, builds the critical systems, and stays accountable through launch.
It starts with a two-week AI Shipping Sprint: one senior engineer, a fixed $7,500, no equity, no lock-in. A scoped feature shipped to production, not another open-ended engagement.
What I help you ship.
Three ways I put AI to work inside an established product and team.
Add AI to your product
Ship search, recommendations, assistants, or workflow features that customers can actually use.
Automate expensive manual work
Turn repeatable internal processes into reliable systems without adding headcount.
Help your team ship faster
Introduce AI-assisted engineering workflows and reusable foundations without lowering the quality bar.
Ways to work together
One entry point, one delivery path, and one ongoing partnership. Designed for companies that already have a product and a capable team.
Product judgment and technical ownership, not novelty for its own sake.
The best AI partner is not the person who knows the most model names. It is the person who can understand the business, choose the right leverage point, and turn it into a production result.
Builder and operator who has repeatedly stepped across product, engineering, leadership, and delivery boundaries
Comfortable owning ambiguous AI opportunities and turning them into production systems
Works with capable teams that need senior judgment and hands-on execution, not more management overhead
Have an AI feature your team has not shipped?
Bring me the opportunity, the bottleneck, or the messy first draft. We will determine what is worth building, what production requires, and whether a focused engagement makes sense.
No strategy theater. No obligation. Just a direct conversation about the work.