About Rayan

7 items Rayan Sadri Toronto

Rayan Sadri

Applied AI researcher turned founder operator.

I co-founded Carez AI, a synthetic medical imaging company backed by ERA NYC, CDL AI, NEXT36, and Remarkable Ventures.

Before that, I researched AI model behavior, data quality, and bias at McGill.

I now work where technical products meet customers: AI deployment, GTM systems, workflow automation, customer discovery, and narrative.

Useful for

Customer discovery, technical scoping, AI workflow prototypes, enterprise demos, GTM systems, deployment plans, and product narrative.

Profile Rayan_Sadri

Proof Stack

12 items Proof Stack Signals

Places I’ve built, researched, spoken, or been backed by.

Carez AICo-founder, synthetic medical imaging.

McGillApplied AI research and software engineering.

PassageGTM Engineer, Solutions Architect.

ERA NYCBacked founder, NYC accelerator.

CDL AIAI stream, technical founder program.

NEXT36Selected founder, Canadian fellowship.

McGill EngineTechAccel startup mentor.

New York TimesFeatured.

Seattle TimesFeatured.

United Nations / WIPOSpeaker on synthetic media and IP.

McGill DelveFounder feature.

9.1M X impressionsOrganic distribution from founder writing.

Proof_stack.list 12 items

Carez AI

4 metrics Carez AI Founder proof

Carez AI

Co-founded a VC backed synthetic medical imaging company. Led product and GTM, grew the team to five, engaged 35 plus enterprise teams, built $500K plus pipeline, and shipped synthetic CT/MRI infrastructure for AI model training and validation.

35+enterprise teams engaged

$500K+pipeline built

4.2M+CT/MRI slice equivalents

5person team built from zero

Owned

Product, GTM, demos, customer discovery, technical scoping, security review motion, buyer narrative, and deployment workflows.

Carez_AI.folder Product + GTM

AI Research

135,556 rows McGill Research

McGill AI Research

Applied AI research on model behavior, data quality, evaluation, and bias in automated hate speech detection.

135,556annotated research rows

NLPclassification and model behavior

Biasdata distribution and fairness analysis

Evalprecision, recall, F1

Translation

That research foundation now shows up in how I think about AI workflows: data quality, evaluation, failure modes, human review, and where models actually fit inside an operating system.

McGill_research.folder AI research

Automation Receipts

3 demos Automation Workflow tools

Small systems for messy work.

Enterprise Call → Buyer Brief

Raw customer calls become buyer pain, stakeholders, objections, urgency, next step, and follow-up.

Assessment Intake → Decision Packet

Forms, docs, policies, and notes become reviewer summary, risk flags, missing info, and next action.

GTM Research → First Message

Company URL and buyer context become account research, wedge, custom first message, and follow-up queue.

Pattern

Messy input → structured output → human decision → monitor and improve.

Automation_receipts.folder 3 demos

Contact

4 links Contact Open

Contact

Useful for AI deployment, GTM systems, solutions, customer discovery, workflow automation, and technical product narrative.

Emailrynsdri@gmail.com

LinkedInlinkedin.com/in/ray-sadri

X@rayansadri

GitHubgithub.com/rayansadri

Line

Researched the AI. Built the product. Sold the product. Explained the product. Deployed the workflow. Made people care.

Contact.card 4 links