Forge Applied Science & TechnologyAI Implementation · Feasibility Studies

About

Where medicine, engineering, and operations meet.

Forge Applied Science & Technology exists to solve problems that don’t fit neatly in one discipline — and to do it with the rigor those problems deserve.

John-Paul (JP) Bonansinga

John-Paul (JP) Bonansinga, M.D.

Founder & Principal — Physician-Scientist, Lean Six Sigma practitioner

JP founded Forge to bring engineering discipline and clinical judgment to the same table. He is a physician-scientist with graduate training in advanced manufacturing, artificial intelligence, and Lean manufacturing — a combination built to translate between the technical and the human sides of an operation.

His path has been anything but linear. He led teams in some of the most demanding environments imaginable before earning his medical degree, then ran a federal defense research-and-development company that designed, validated, and manufactured hardware across the life and physical sciences. That arc — from austere, high-stakes leadership to the clinic to the lab to the production line — is the reason F.A.S.T. can sit comfortably at the intersection of science, regulation, and operations.

Today he focuses on helping organizations — especially in healthcare — adopt AI and Lean Six Sigma the right way: measured, humane, and grounded in real numbers.

John-Paul Bonansinga presenting the RLX sterilization program at a biomedical innovation symposium
Presenting the RLX program at a biomedical innovation symposium.

Credentials

Trained to bridge worlds

  • Doctor of Medicine (M.D.)
  • B.S. in Neuroscience
  • Graduate coursework in Advanced Manufacturing & Enterprise Engineering — AI/ML and Lean
  • Lean Six Sigma practitioner (DMAIC, VSM, COPQ)
  • Named inventor on multiple U.S. and international patent filings
  • Former CEO of a federal defense R&D contractor (Life and Physical Sciences)

How we work

Principles that don’t bend

  • Measure before you change. We don’t automate a process we haven’t mapped.
  • Tie it to the numbers. Every recommendation traces to a figure you can verify.
  • Respect the people. The goal is to remove overburden, not replace the team.
  • Transparent pricing. Fixed fees, clear scope, no surprises.
  • Earn the next step. Discovery first; you decide what comes after.

Build with a partner who has actually built things.

From validated medical hardware to AI-driven operations, the through-line is the same: do it carefully, prove it works.

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