Olga Lavinda, PhD · Verifiable AI

Most AI can't be trusted.
I build the kind that can.

AI now produces answers faster than anyone can check them. In health, safety, and science, an answer you can't verify is worse than none. I close that gap end to end: I design the evidence, build the product, and publish the methodology that proves it.

Work with me → Strategy · AI validation · Speaking · Writing
Olga Lavinda, PhD

"I turn clinical evidence into health products people can use. Then I publish the methodology so you can verify it."

The point of view

The hard part of AI isn't building it. It's trusting it.

Models are fluent, fast, and confidently wrong. The scarce skill is no longer generating an answer. It is knowing which answers hold up, and being able to prove it to a regulator, a clinician, or a parent. I build that layer, and I am rare because I do the whole chain myself: the science that decides what is true, the system that ships it, and the published proof that stands up to scrutiny.

Who this is for

Teams shipping AI that can't afford to be wrong.

Founders, clinical and regulatory leaders, and research groups in health, safety, and regulated industry, who need their AI validated and defensible, not just demoed. I work as both strategist and hands-on builder.

Strategy & fractional chief-scientist AI validation & evaluation Evidence architecture Speaking Writing & research
The proof

It's already shipped.

One engine runs under all of it: it turns public safety and regulatory data into working products, product and food recall intelligence, ingredient safety, FDA device enforcement, even a tire scanner with roughly 500 real scans. Each is exposed as an MCP server any AI agent can call directly.

Clarity, the shipped ingredient-safety app
Clarity, live and in use. Evidence-first, scientist-built.
Clarity, a four-million-product safety engine
Clarity is a safety engine I designed and shipped, with a public API, that lets anyone check what is really in four million products. Every ingredient is graded across 25+ dimensions on published, evidence-based methodology, not a generic AI guess. The thesis, working in production.
See Clarity live →
483 Risk Radar, FDA compliance intelligence
An automated FDA enforcement-intelligence pipeline for medical-device makers. It ingests recalls, adverse events, warning letters, and inspection citations from four public sources, scores escalation risk by category, and monitors it week over week. Live in production on Cloudflare, built solo, and exposed as an MCP server any AI agent can query directly.
See 483 Risk Radar live →
RIGOR, my AI-validation framework
Chosen over Amazon, Microsoft, IBM, SAS, NTT Data, Dell, and Oracle to build an AI early-warning system for a Fortune-class manufacturer. The same evidence-first method that powers Clarity.
See the framework →
The Lavinda Lab, chemistry-aware AI validation
Where I stress-test AI in the hardest setting there is: atomic-resolution biochemistry. The academic proof of the same thesis, run with students.
See the science →
Background

PhD in chemistry, NYU. NIH Kirschstein NRSA Fellow. Eugene M. Farber Award, Society for Investigative Dermatology. Seventeen publications, and four ACS talks in 2026, including an invited COMSCI symposium and a Sci-Mix selection. Founder and principal scientist of Health AI. Member of the Coalition for Health AI, the American Chemical Society, and Women in AI.

ORCID · Scholar · LinkedIn · Research Lab · Health AI · GitHub

Let's build something you can trust.

Strategy, AI validation and evaluation, evidence architecture, speaking, and writing. Available now for the right projects.

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Also building: adulting.nyc, data-driven decision tools for NYC families (1,843 public schools).

© 2026 Olga Lavinda · Health AI LLC