Forward deployed engineer.
I implement AI systems at scale.
I’m a forward deployed engineer. I embed with your team — inside your tools, your data, your day-to-day operations — and build the AI systems that own real work end-to-end.
These are production systems, not prototypes: they ingest live inputs, apply decision logic, and take action without a human triggering each step — engineered to stay reliable as volume grows and to scale with the business instead of stalling at the demo.
Where businesses lose the most
Manual review and approval queues
Pattern-based decisions executed by humans on every occurrence. Returns, applications, inbound requests, status updates. When the outcome is structurally predictable from the inputs, the process belongs to an agent.
Slow response to customers and leads
Every hour a qualified lead sits without contact is measurable revenue exposure. Every support request left pending is an attrition signal. Response velocity used to be a function of team size. It no longer is.
Staff operating below their leverage point
Data entry, report generation, scheduling, routing, follow-up sequences. High-frequency, low-judgment work that saturates capacity and crowds out the decisions that require real expertise.
Operations that cannot absorb volume growth
When scaling revenue requires scaling headcount proportionally, margin compression is structural. Businesses building durable leverage are separating output from labor by deploying agents to handle the repeatable work.
The ontology
Agents are only as good as the model of the business they run on. Before a single agent ships, I build an ontology — a structured map of how your operation actually works: the objects it runs on, the properties that matter, the relationships between them, and the rules behind every decision.
Model the business, not the prompt
Most agents fail in production on context, not capability. An ontology gives them the whole picture — what an object is, what state it is in, what it connects to, and what is allowed to happen next — so every decision is grounded in how the business actually operates.
Agents act on the model
With the ontology in place, agents stop guessing. They read real state, apply the logic already encoded in the model, and take action end-to-end — the same way, every time, at any volume.
Efficiency that compounds
Every new agent reuses the same ontology. The second workflow is faster to automate than the first; the fifth faster still. The model is the durable asset — the agents are what it pays out.
Journal
Read the journal →Notes on how AI systems actually get built, and field reports from the ones running in live operations — the problem, what got built, and what changed once it shipped.