AI innovations,
across industry.
A portfolio of AI applications and agent architectures I've designed across industries, some shipped to production, others mapped out as blueprints. Organized by the business problem they solve, not by the model that happened to be in fashion.
The interesting question was never "where can we add AI?" It's which problem in this industry is really a reading, reasoning and form-filling problem in disguise, and then designing the system that solves it.
What I've built,
and what I'd build.
A mix of systems shipped to production and agent architectures designed for the problem, each tied to the industry it serves and the business outcome it moves.
An agent team for tax & filings
A graph of narrow AI specialists, tax-law, accounting-data, deductions, compilation, a critical thinker, an auditor and an orchestrator, that reads the data, computes against the rules, and drafts the filings, with the accountant accountable at the end.
- →One tax-law agent per section: personal, corporate, trusts
- →A deductions agent that hunts for legitimate savings
- →A graph, not a swarm, so every conclusion is traceable
- →Human signs off on a summary of the reasoning, not raw data
Margin-finding agents for fulfillment
In a commodity market the price is fixed, so profit lives in the cost of fulfilling each order. A graph of agents sources every line, across owned inventory and drop-ship suppliers, picks the cheapest carrier that still meets the promise, and holds every order above a margin floor.
- →Sourcing agents across owned stock and drop-ship suppliers
- →Carrier-rate agents pick the cheapest path that meets the promise
- →A landed-cost agent computes true margin per fulfillment plan
- →A margin guardrail blocks any order that would ship at a loss
From problem to system.
Start from the problem
I start from the business outcome, not the model. The first question is which work in an industry is really reading, reasoning and computation in disguise, and where AI is just a distraction.
Design the agent system
Most of these are agent architectures: narrow specialists organized under a hierarchy you can trace, designed so the output is something a professional can defend, not a confident guess.
Ship where it counts
Where a design goes to production, it ships with evaluation, observability and cost control, and a human kept at the point where judgment and accountability belong.
What teams ask about this work.
Have you shipped all of these, or are some designs?
Both. Some are systems running in production; others are agent architectures I've designed for a specific problem, blueprints ready to build. Each entry is clear about which it is.
Can you adapt one of these to my industry?
Usually, yes. The pattern, narrow specialist agents under a traceable hierarchy with a human accountable at the end, transfers across domains. The work is mapping it to your data, your rules, and your regulatory reality.
Do you build, or only design?
Both. I work hands-on, architecting and, where it makes sense, building and shipping the system with evaluation, observability and cost control. Some engagements stop at a design and a roadmap your team executes.
How do you keep these reliable in a regulated industry?
By organizing agents as a graph rather than a swarm, so every conclusion is traceable, and by keeping a human at the end of the process to review the reasoning, not just the result.
Have a problem
that AI could solve?
Tell me the industry and the outcome you're after. I'll tell you straight whether AI is the right tool, and what it would take to design and ship it.