Service manifest
Stop pilot purgatory.Start shipping.
Most AI projects die between demo and production. The gap isn't technical skill — it's knowing what actually matters at scale.
I've shipped LLM systems since GPT-3. I know what breaks.
Capabilities
Three things. Done well.
Production Architecture
Most teams ship the demo and call it done. Then production happens.
- →RAG pipelines that don't hallucinate at scale
- →Vector databases handling actual QPS
- →Evaluation loops that catch failures first
Why this matters: The gap between "works locally" and "works at scale" is where projects die.
Technical Due Diligence
Most AI moats are demos with good lighting. I find the real ones.
- →Architecture reviews beyond "using GPT-4"
- →Team assessment: can they ship or just prototype?
- →Technical debt mapping before it's your debt
Why this matters: Bad diligence means inheriting someone else's mistakes.
Fractional AI Leadership
You need senior technical direction. You don't need another $500K salary.
- →Architecture decisions that last 18+ months
- →Hiring support: who to bring in, who to pass
- →Hard conversations your team won't have
Why this matters: The gap between vision and reality kills companies.
The playbook
Proven process, not improvisation.
Diagnostic assessment
I analyze your current AI capabilities, identify bottlenecks, and establish clear metrics. No assumptions — just data.
Metrics framework
Custom measurement systems that link technical performance to business outcomes. If you can't measure it, you can't improve it.
Implementation strategy
A tailored playbook with specific technical approaches, evaluation frameworks, and team workflows. Not templates — solutions.
Embedded execution
I work directly alongside your team. PRs, standups, Slack. Knowledge transfer is built in, not bolted on.
Continuous optimization
Monitoring systems and feedback loops for sustained improvement. The work doesn't stop when I leave.
Ideal fit
Not everyone. The right ones.
You're a good fit if
- →You're stuck moving AI from POC to production
- →Your team knows software but not probabilistic systems
- →You need senior direction without full-time overhead
- →You want your team to own it, not depend on consultants
We're not a fit if
- ✕You want someone to build it and disappear
- ✕You're looking for magic, not methodology
- ✕You need a body shop, not strategic direction
- ✕Budget is the primary decision factor
Honest filtering saves everyone time. If it's not a fit, I'll tell you.
Operating principles
Simple baselines first
Start with the simplest solution that could work before adding complexity.
Debuggable systems
Build AI systems that are easy to understand, debug, and improve. No black boxes.
Outcomes over theory
Ground all AI initiatives in real-world applications that deliver tangible results.
An investment in regret minimization
I've seen companies waste months and millions on AI initiatives that go nowhere. I can't promise product-market fit, but I can promise clarity.
My job is simple: tell you when what you think is easy is actually hard, and when what you think is hard is actually easy.
Ready to start?
Let's talk about your project.
30-minute strategy call. No pitch deck. Just a real conversation about what you're building.