Building AI systems that actually ship.
MLDeep exists to build AI systems that reach production -- not AI theater that stalls in staging.
I work with Series A founders who need one painful workflow automated, and Series B/C heads of data who need a clear verdict on whether their stack can support production AI. One engagement at a time. The practitioner who scopes it builds it.
I spent years watching AI projects fail in the same way.
Not from bad technology. From bad delivery.
The senior partner closes the deal. The junior analyst does the work. The consultants rotate off when the engagement ends. The client is left with a slide deck, a roadmap nobody owns, and no way to run it. I watched this happen across organizations that spent real money expecting real AI -- and walked away with AI theater.
I was also on the inside of it. I was the practitioner doing the work the partner sold. I sat with data teams who had paid for six-week engagements and received artifacts they couldn't maintain. I built things that were handed off, never touched again, and quietly deprecated 18 months later.
"The person you speak to on the fit call is the same person writing the code."
MLDeep is built to break that pattern. The person you speak to on the fit call is the same person writing the code, making the tradeoffs, and handing off the documentation. If something breaks after delivery, I'm reachable. If I can't help you, I'll tell you in the first call.
That's not a differentiator. It's a minimum standard I refuse to work below.
If this sounds like the engagement you've been looking for:
Background
- 9 years in data engineering, cloud infrastructure, and AI
- dbt Labs Registered Consulting & Services Partner
- Published researcher -- AgentDoctor, a framework for diagnosing and repairing autonomous AI agents
- Delivery partner with Domain Methods for engagements that need extended capacity
- Full-stack: data modeling (dbt), cloud infrastructure (Terraform), AI agent design and deployment
- Clients are B2B SaaS teams at Series A through Series C
Specialist consultancy. One engagement at a time.
When one person spans data engineering, cloud infrastructure, and AI agents, there are no hand-offs. No context lost between teams. No "that's not my department." The person who understands your data warehouse is the same person who builds your AI agent, deploys it to production, and hands it off documented.
Data Engineering
dbt implementation, data modeling, pipeline orchestration, data quality testing. The foundation that makes everything else possible.
Cloud Infrastructure
Terraform, CI/CD, cloud governance, security hardening. Infrastructure as code, not infrastructure as guesswork.
AI Agents
Design, build, and deploy production-grade AI agents. From architecture to monitoring, one practitioner end-to-end.
Bring me one real problem.
Tell me the workflow that's wasting time or the AI initiative that's stalled. I'll tell you whether it looks like a sprint, a diagnostic, or not a fit -- usually in the first call.