Andrew Bottoni

I'm Andrew Bottoni — Andy, to everyone who works with me. I solve engineering problems that don't come with a template: systems that have to be right, not just impressive, where a confident wrong answer costs real money and the engineering that catches it matters more than the component that produced it.

My practice is not retrospective. I am actively building new tools, testing new technologies, and learning the disciplines that shape the work. I also remain an active Mobile Intensive Care Paramedic. The long record gives me more failure patterns to recognize; current practice keeps me from trusting any pattern without checking it against the conditions in front of me.

Most weeks that means three different kinds of work at once. A legacy system being rescued — a generated low-code application or a thirty-year-old desktop database — rebuilt as something modern without stopping the business that depends on it. An integration between systems that were never designed to speak, which is where most projects quietly fail. And the infrastructure underneath all of it: lately, a distributed AI farm I own outright. Not to stop using frontier models — I use several deliberately, and check their findings against each other — but so that where a workload runs, and what leaves, is mine to decide rather than a vendor's to assume.

The finance work is the part people know me for, and it is genuinely a third of it. I was wiring live market data into portfolio systems in the 1980s, at Apple's invitation, on the original Macintosh. My current research system is an orchestrated hierarchical multi-agent system for systematic investing: a coordinator delegating to tiers of local and cloud models, paired with a software-run statistical evaluation layer that decides what advances. But the interesting engineering there isn't the agents, and it isn't the finance — it's the machinery that makes the claims of fallible components visible and testable. That discipline is domain-agnostic, and it is the part clients can use.

Because language models can produce plausible, confident, and unverified answers, the open engineering question is the harness: the checks, boundaries, and operating evidence that let you build on them anyway. I run that investigation as engineering: instrument the system, re-evaluate it ruthlessly, publish the unflattering findings, and convert every discovered weak point into a permanent machine-checked safeguard so the floor only rises. What comes out applies to any system where a confident wrong answer is expensive — which is most systems worth building.

I've been a working programmer since 1979 — no degree behind it; the record is the credential, starting with an invited Apple engineering residency in 1984. Portfolio and investment systems, payments, cross-platform desktop, native mobile, safety technology, and current AI systems all belong to the same active practice. The engineering heritage has the full record and the story of how the two careers continue to feed each other.

Still building. Still learning. Still accountable.

Continuing education is part of both professions. Renewing my New Jersey paramedic license requires ongoing classes as medical knowledge and treatment protocols change. I apply the same discipline to engineering through formal coursework such as MIT's 6.00.1x, Introduction to Computer Science and Programming Using Python, and through a daily research habit spanning AI developments, market trends, new tools, and lectures in science, computer science, and mathematics.

I do not follow those developments merely to remain informed. I identify ideas worth testing, put the promising ones into working systems, and observe where they survive or fail. AI TraceVector Alpha is evidence of that cycle in practice. My value is not simply knowing about the newest technology; it is deciding what deserves attention, what should be tested, what is ready to use, and what should be rejected.

What I'm good at

• Whole-system diagnosis — decades of finding the failure nobody else is looking for, in software and in the back of an ambulance: reading a system end to end, refusing the comfortable explanation, and tracing symptoms to root causes — including the ones hiding in what isn't there.
• Rapid delivery of complex multi-system builds — front end, back end, schema, and the API integration layer, delivered as one working system rather than three teams' worth of handoffs. Legacy migrations, PWAs, services against live production databases, and cutovers that don't stop the business.
• Inventive solutions to unsolved problems — when the diagnosis has no off-the-shelf answer, designing one: review systems, acceptance checks, and architectures that keep unreliable components from hiding their failures.
• Self-hosted AI infrastructure — inference fleets on owned hardware, gateway routing across local and cloud tiers, model selection by measured fitness rather than parameter count, and the unglamorous hardware bring-up that makes it real.
• Quantitative evaluation — permutation tests, deflated Sharpe, bootstrap robustness, walk-forward and regime analysis; turning "trust me" into "re-run this and see."
• Full-stack operations — data pipelines, dashboards, automation, alerting, and the operational discipline to run them unattended.

How I work

Decompose the problem into units, deal with each one surgically, verify it independently, then compose the larger thing out of verified parts. It is why large changes can move quickly without becoming unreviewable — every line traces back to something that was asked for. And every defect found becomes a check that outlives the fix, so the same failure cannot return quietly later. There is a longer version of this, with the twelve systems it currently applies to, on the practice page.

Two reasons you might be here

You have a hard problem. If you're a company facing something that doesn't fit an off-the-shelf tool — a migration nobody wants to own, an integration that keeps failing quietly, a build with a date already promised, or AI infrastructure you'd rather own than rent — that's exactly the work I want. I operate through MICP, my engineering practice.

You're stuck on something I've solved. I write up the non-obvious problems and their fixes in my notes, document the larger system in the HMAS white paper, and keep the hardware war stories on the farm page. If it saves you a week, that's the point.