Agentic AI for life sciences R&D

Useful, not clever.

I help life sciences leaders use agentic AI to make R&D teams dramatically more capable and get better products to market faster.

The goal is not more AI activity. It is more product velocity: better decisions, less friction, stronger computational teams, and more time focused on the assay, instrument, evidence, and product that actually create value.

See how I can help See what I've built

Why me

I have built the teams and systems behind real diagnostics products.

I have spent 16 years building computational R&D, software, data, diagnostics products, and technical organizations across startups and scaled companies.

Built from zero to launchRepeatedly built teams, software, data systems, and diagnostics products from inception through commercialization.

R&D, not demosRUO, IVD, LDT, solid tumor, liquid biopsy, hematologic malignancies, immune repertoire, regulated software, and clinical evidence.

Whole product thinkingAssay, computation, software, validation, regulatory evidence, vendors, IP, and commercialization.

Scar tissue includedOverbuilding, scaling too early, bad technology choices, under-testing, FDA and MolDX pain, and the lessons that came with them.

How I think about software

Boring software. Exceptional science.

Support the product

In most life sciences companies, software is a means to an end. The differentiated value is the assay, instrument, evidence, and product that reaches patients.

Build only where it matters

Great engineers love to build. That can become a liability when bespoke software creates years of maintenance without creating differentiated business value.

Buy at the right time

Buying too late wastes engineering effort. Buying too early, before workflows stabilize, can lock an R&D lab into a vendor and a process that no longer fit.

Use AI to increase product velocity

Agentic AI should help scientists and computational teams move faster through knowledge, analysis, software, and evidence. If it does not improve the path to product, it is probably noise.

The goal

Magnify the talent already inside R&D.

Life sciences companies already have people who know how to invent assays, discover biology, build instruments, analyze studies, navigate regulation, and make hard product decisions. I help leadership apply AI and computational strategy in a disciplined way so those people can move faster on the work that makes the company special.

The technology should serve the product, not become a second product company inside the first one.