Seven years building ML systems in production. I know where the gap is. It is almost never the model.
The POC worked. Then you tried to connect it to real data, real users, and real load. A model that performs in isolation and a model that performs in production are two different engineering problems. Most teams don't find out until it's expensive.
LLMs are not the answer to every problem. Neither is a vector database. Neither is an agent. Most projects that fail do so because the wrong tool was chosen before the problem was fully understood.
AI systems degrade. Models drift. Data distributions shift. The team that built it moved on. If there's no plan for what happens after launch, you will be rebuilding in 18 months.
Optimizing for the wrong metric is worse than not optimizing at all. 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.
Production-grade retrieval-augmented generation on your data. Custom knowledge bases, semantic search, document Q&A, and grounded generation that doesn't hallucinate. 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.
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.
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 we ship. You know what good looks like so you know when it stops.
Monitoring and alerting included. No fire and forget.
Companies that hit the ceiling on off-the-shelf AI tools. Founders who built a POC and can't figure out why it doesn't work the same way on real data. Technical teams that know they need AI in the stack but don't have the ML background to architect it correctly. Organizations that have been burned by a flashy demo that never made it to users.
Define the hardest question. Build the smallest thing that answers it. 2-4 weeks, fixed scope, written recommendation at the end. Right for companies that need to validate before committing a full budget.
Architecture through production deployment. Discovery, build, monitoring setup, and operational documentation. 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. Right for product teams who need ML capability without the overhead of a full-time hire.
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 2-4 months depending on data complexity, integration requirements, and whether the problem definition is solid before we 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 we bring into the engagement.
Yes, but the specific requirements need to be on the table before we start. Compliance constraints shape architecture decisions from day one. I have experience building AI systems under HIPAA, SOC 2, and GDPR requirements.
Book a working session. Bring the problem you're trying to solve, what you've already tried, and the constraints that matter. I'll tell you within 60 minutes whether it's a solvable problem and what the right architecture looks like.