Seven years building ML systems in production. I know where the gap is. It is almost never the model.
A model that performs in isolation and a model that performs in production are two different engineering problems. Most teams don't find that out until it's expensive.
Most AI projects fail because the wrong tool was chosen before the problem was fully understood, not because the team couldn't execute.
Models drift. Data distributions shift. The team moves on. Without a plan for what happens after launch, you will be rebuilding in 18 months.
If nobody defined what correct looks like in production terms before the build started, you can't know whether you shipped something that works.
Autonomous agents that reason across tools, APIs, and data sources. Multi-step task execution, tool use, memory, and orchestration. Built to handle the complexity your users should never have to see. Built multi-agent systems that replaced three-person ops workflows for teams under 50 people.
Production-grade retrieval-augmented generation on your data. Custom knowledge bases, semantic search, document Q&A, and grounded generation that doesn't hallucinate. Shipped document Q&A systems that run against proprietary data without surfacing out-of-scope responses. Built against your compliance constraints from day one.
Classical ML through deep learning for structured prediction, anomaly detection, classification, and forecasting. The right model for the problem, not the most impressive one for the demo. Built forecasting systems that replaced manual reporting processes taking days each week.
Object detection, image classification, quality inspection, document parsing, and visual search. If your problem involves images, video, or documents with layout, this is the layer. Shipped inspection systems that caught defect patterns human review was missing.
The systems that keep AI working after you ship it. Model serving, monitoring, retraining pipelines, evaluation frameworks, and deployment architecture. Build it right once so you don't rebuild it in 18 months.
Before a model is selected or a dataset is touched, I need to understand the problem in production terms. What does correct look like? What does wrong look like? What happens when the system is uncertain? Most AI projects skip this. That's why most fail.
One week. Written output: problem definition doc and success criteria.
I build the smallest possible thing that answers the hardest question about your problem. Not to impress you. To find out what's actually hard before you commit budget to it.
2-3 weeks. Written output: build/no-build recommendation with reasoning.
Engineering with full test coverage, monitoring hooks, and operational documentation. Built to be maintained by someone other than the person who built it. Weekly progress reviews throughout.
Timeline scoped before work begins. No surprises.
Launch is not the finish line. I set up evaluation pipelines, drift detection, and performance baselines before anything goes live. You know what good looks like so you know when it stops.
Monitoring and alerting included. No fire and forget.
The API wrappers got you to demo. They won't get you to production. You need something built for your data, your volume, and your compliance requirements.
You know how to build software. You don't have the ML background to architect an AI system correctly. You need someone who can own that layer and make it something your team can maintain after.
The vendor showed you something impressive. It didn't survive contact with real data. You need someone who builds for production from day one, not someone optimizing for the next meeting.
Define the hardest question. Build the smallest thing that answers it. 2-4 weeks, fixed scope, written recommendation at the end. Right for business owners who need to validate before committing a full budget.
Architecture through production deployment. Discovery, build, monitoring setup, and operational documentation. Typically 3-5 months depending on complexity and whether the problem definition is solid before we start. Timeline and scope defined before work begins. No open-ended billing.
One senior ML engineer working inside your team on your tools, your sprints, your lead. Brings ML judgment to teams that need the capability without the risk or overhead of a full-time hire. Right for product teams who need to move now without a permanent headcount decision.
When the data doesn't exist, when the problem is actually a process problem, or when the cost of building something custom exceeds the cost of buying something that already works. I'll tell you at the start of the engagement if I think that's the case.
A POC is 2-4 weeks. A production system is typically 3-5 months depending on data complexity, integration requirements, and whether the problem definition is solid before I start. I won't give you a timeline until I understand the problem.
Almost never the model. Usually: data quality problems that didn't surface in development, integration assumptions that broke under real load, or no monitoring in place to catch when things drifted. All three are preventable if you build for production from the start.
Yes. Most clients start from zero. Infrastructure decisions are part of the architecture phase, not an assumption I bring into the engagement.
Yes, but the specific requirements need to be on the table before I start. Compliance constraints shape architecture decisions from day one. I have experience building AI systems under HIPAA, SOC 2, and GDPR requirements.
A POC Build is fixed scope with a defined price agreed before work starts. End-to-end builds are scoped by phase. You know what each phase costs before you commit to it. I don't bill hourly and I don't do open-ended retainers.
Advisory is for owners who need to figure out where AI fits and whether it's the right move. Development is for owners who have already decided to build something and need someone to build it. If you're not sure which one you need, start with the discovery call.
No. Data readiness is part of what I assess in the problem definition phase. Most owners don't know what data they have or what shape it's in until someone looks. That's one of the first things we figure out together.
Yes. The embedded ML engineer model is designed for exactly that. I work inside your existing workflow, not parallel to it.
Performance baselines and drift detection are built in before launch, not added after. You know what good looks like from day one. If something degrades, monitoring catches it before your users do.
Before any work begins, we agree on what the first phase delivers and what it costs. A written scope: the problem we're solving, how we'll know it's working, what it costs to get there. Every phase ends with a defined output. You see what moved before you decide whether to continue. Nothing gets added to scope without your sign-off.
Book a free discovery call. Bring the problem you're trying to solve, what you've already tried, and the constraints that matter. I'll tell you within 30 minutes whether it's a solvable problem and what the right architecture looks like.