Meridian Applied is a physician-led practice that deploys AI, quantitative, and data systems into high-stakes environments. We build working systems, tested against reality, not decks.
Most AI never leaves the demo. The gap is rarely the model. It's the data plumbing, the evaluation, and the judgment about where AI belongs and where it doesn't. That gap is the whole job.
Retrieval systems over your own material, agent workflows, and fine-tuned models, built with the evaluation harnesses that prove they're reliable before anyone leans on them.
Financial modeling, statistical analysis, and decision-support built on real data. From optimization models to backtested strategies to dashboards leadership actually reads.
Designing how the pieces connect: data standards, integration between platforms that were never built to talk, and roadmaps that survive contact with a real organization.
For environments where sensitive data can't leave the building, we deploy locally hosted open-weight models that keep your data yours, without giving up capability.
The first version is built for one organization, against their actual systems, until it produces something they'd stake a decision on. Patterns get reusable only after they've earned it.
A system that's right most of the time is a liability where the stakes are real. Every build ships with a way to measure whether it's actually working, expressed as a number.
The practice was forged in healthcare and humanitarian operations. Sensitive material is handled with the judgment that background demands, and stays where it belongs.
Designed an AI-enabled architecture connecting three global health information systems. Presented to the Chief Medical Officer and adopted as the organizational deployment roadmap.
Retrieval systems that let front-office staff ask plain-language questions of their own scouting and research material and get trustworthy, sourced answers on the clock.
Serving as AI implementation specialist for a university civil and environmental engineering department, bringing practical generative AI into research and operational workflows.
Portfolio optimization and backtesting frameworks, and analytical tooling that turns messy inputs into decisions leadership can defend.
Two peer-visible papers proposing structural fixes for how health data moves between systems, grounded in front-line operational experience.
Meridian Applied is principal-led. The work is done by someone who sits at an unusual crossroads: a physician who builds AI systems himself, in code, and has deployed them where a wrong answer has real consequences.
That combination is the point. Clinical judgment about what "good enough" means when stakes are high. The engineering to build the thing rather than specify it. And the executive fluency to sit across from a chief medical officer, a general manager, or a head coach and translate an ambiguous problem into something that ships.
Specialist collaborators are brought in when a project calls for it. The accountability stays in one place.
The best engagements start with a specific, stubborn problem. Bring yours and let's see if there's a system in it.
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