I build AI systems
that survive Monday morning
Over 8 years in technology and AI, I’ve learned that the gap between a working demo and a production system is where most AI projects die. I exist to bridge that gap.
Most AI projects fail because they solve the wrong problem
I start every engagement by listening. Not to your tech stack preferences or your board’s AI mandate -- but to the actual business problems that keep your team up at night.
From there, I work backwards to the simplest technical solution that addresses the root cause. Sometimes that’s a custom ML pipeline. Sometimes it’s an off-the-shelf API wired into your existing workflow. Occasionally, it means telling you AI isn’t the answer -- and saving you six months of misguided development.
Every engagement follows four phases. First, an AI audit -- we map where you are today, where you want to go, and what use cases exist across your business. Second, use case prioritization -- we evaluate each opportunity on value, effort, and cost, then decide which ones to tackle first. Third, we build -- using the right tools for the job or developing the AI agents your use case requires. Fourth, implementation -- we roll it out and make sure your team knows how to use what we built.
Fewer clients, deeper impact
I don’t optimize for volume. I work with a limited number of companies at a time, on a fixed budget with no hour counting. That means I’m not watching the clock -- I’m watching the outcome.
I’m typically on-site at least once a week. Not because remote doesn’t work, but because AI projects don’t follow a straight line. A new model launches and suddenly the architecture we planned last month has a better option. The use case we scoped shifts because the technology caught up faster than expected. The system we were building might be outdated before it ships.
If you treat an AI project like a traditional IT project -- big upfront analysis, rigid scope, fixed deliverables -- you’ll almost certainly build the wrong thing. By the time you’re done, there’s a new way of solving the problem. Being present means I can pivot with you in real time, not three change requests later.
That’s why the model is fixed budget, not hourly. When a multimodal model drops and we can consolidate three separate models into one -- saving tokens, reducing complexity -- that’s a conversation over coffee, not a billable scope change. The incentive is aligned: deliver the best result, not the most hours.
What I believe
Ship outcomes, not demos
A proof of concept impresses in a meeting. A production system changes a business. I optimize for the second.
Simplicity compounds
The right solution is the simplest one that works. Complexity is easy to add and hard to remove.
Transfer ownership, not dependency
Every engagement ends with your team understanding the system. I build capability, not consulting hours.
Honest about limitations
Sometimes the answer is that AI isn’t the right tool. That honesty saves months and millions.
From engineering to strategy and back
I’ve spent over 8 years working across fintech, retail, healthcare, and industrial manufacturing. Before consulting, I built and led technical teams -- which means I can discuss ROI with executives and architecture with engineers in the same afternoon.
That dual perspective is rare, and it’s the reason my projects actually make it to production.
Let’s talk about what you’re building
Whether you’re exploring AI for the first time or scaling what’s already working, I’d like to hear your story.
Start a conversation