Rethinking Regulatory
Preparation
We're building Veridocx to make 510(k) preparation faster and more defensible—without replacing the expert judgment that matters most.
Our Mission
Regulatory preparation for medical devices is essential—but it doesn't have to be as slow and fragmented as it is today.
We're building Veridocx to give RA/QA professionals the computational tools they need: credible starting points, structured evidence, and exportable rationale—all grounded in FDA data.
Our goal isn't to replace expert judgment. It's to augment your team's capabilities so you can focus on strategy, submission quality, and regulatory judgment.
Regulatory Rigor
Every output meets the standards regulatory teams apply to FDA submissions.
Full Transparency
Complete audit trails, FDA citations, and reasoning chains for every recommendation.
Speed & Precision
Hours instead of weeks—without compromising the rigor required for submissions.
Supported by Leading Institutions
Our team and vision have been nurtured by prestigious technology and health-innovation programs across Canada.
About the Team
Veridocx was built by a team from University of Toronto, University of Waterloo, and Arizona State University with backgrounds in Computer Science, Computer Engineering, and Biotechnology. After working on ML research, data engineering, and medical device projects, we identified a critical gap: regulatory preparation is essential but unnecessarily slow and fragmented.
We're applying AI to make 510(k) preparation faster and more defensible—not to replace expert judgment, but to give RA/QA professionals credible starting points, structured evidence, and exportable rationale grounded in FDA data.
Meet the Team
Olivia Charles
CEO/CRO
MHSc Bioethics, Business & Life Sciences, University of Toronto. Research focus: Regulatory frameworks and medical device policy.
Cole Connelly
CFO/COO
Cell & Molecular Biology and Immunology, University of Toronto. Background in life sciences research and medical device development.
Jonas Martins
CTO
Computer Engineering & AI, University of Toronto. Research interests: Knowledge graphs, semantic search, regulatory AI systems.
Maaz Ahmed
Tech Lead
MS Software Engineering, Arizona State University. Technical focus: Distributed systems and regulatory data processing.
Matthew Li
Full-stack Engineer
Computer Science & Business, University of Waterloo. Expertise in full-stack development and regulatory platform architecture.
Partner with Us
Whether you're a startup looking to accelerate your first submission or an established firm seeking to modernize your regulatory workflow, we'd love to talk.