Domain owners
Finance, life sciences, and enterprise operators shape the workflow with end users before code becomes the answer.
Company
GenerativeX combines domain judgment and engineering execution in the same delivery team, so AI systems are designed around how work actually happens in regulated organizations.
Operating Model
Finance, life sciences, and enterprise operators shape the workflow with end users before code becomes the answer.
Forward-deployed engineers translate workflow details into product behavior, integrations, permissions, and review paths.
The team stays close after release, measuring adoption and expanding from the first workflow to the next one.
Leadership
The group is intentionally cross-functional: people who understand front-office work, enterprise product, infrastructure, and the commercial realities of deploying into US companies.

Founder & CEO
University of Tokyo (Economics and Graduate School of Engineering). Former J.P. Morgan IBD — M&A and capital raising advisory. Founded and exited a prior company before establishing GenerativeX in 2023.

CTO
University of Tokyo, Graduate School of Engineering (Technology Management). Former CTO at a technology company. Former ByteDance — tech lead for enterprise products spanning US and China operations.

VP of Finance / Head of US Operations
University of Tokyo, Columbia University (SIPA). Former J.P. Morgan IBD — ECM execution on large-cap IPOs, coverage group M&A advisory. Former IFC (World Bank Group) in Washington D.C., investing in emerging market banking infrastructure.

Board Director
University of Tokyo (Engineering and Graduate School of Engineering). Former Merrill Lynch Japan (now Bank of America Securities) — M&A, IPOs, domestic and cross-border financing.

VP of US Business Development
Johns Hopkins University (MBA). Former Paramount Pictures (data and analytics). FX advisory background — transaction cost optimization and cross-border treasury strategy.

Executive Officer
Held multiple leadership roles in IT service management and infrastructure at Thomson Reuters Japan over approximately 17 years. Subsequently served as CIO at Allianz Partners, overseeing technology organizations in Japan, the UK, and Ireland, followed by leading Cloud Infrastructure Services Japan at Wipro Limited. Currently Corporate Officer and CTO at AXA Life Insurance.
Engineering

Senior Engineer
B.A. and M.A. in English Studies from the University of Opole, Poland. Began his career in the UK as a software test engineer and network engineer, then designed and automated cloud infrastructure at Zego Ltd and Releaseworks Ltd. As a Senior Specialist at Deloitte Tohmatsu Consulting, led cloud architecture design and implementation for enterprise clients in the automotive, financial and insurance industries. Most recently, as Head of Cloud at BiT & Company, oversaw cloud governance and observability.

Engineer
Warsaw University of Technology (Physics, M.S.) and University of Tokyo (Machine Learning & Optimization, Ph.D.). Research experience in optical and solid-state physics at Utsunomiya University and the University of Tokyo.

Engineer
After hardware development and data science experience at Nokia and Sony CSL, researched multi-agent systems, deep learning, and neuromorphic computing at The University of Tokyo's graduate school. Currently leads the design and deployment of AI systems for pharmaceuticals, telecommunications, and finance.

Principal Software Architect
Tsinghua University (Software Engineering, B.S. & M.S.). Former engineer at Recruit Lifestyle developing large-scale B2C services. Tech Lead at Yahoo! JAPAN, launching marketplace business. Later Engineering Manager at ByteDance, leading internal systems and B2B development teams.
Leadership Backgrounds
Enterprise Trust
Deployment, data ownership, and reviewability are treated as part of the operating system, not as a procurement footnote after the build.
01
A certified security program that can support enterprise procurement and risk review.
02
On-premises and single-tenant deployment paths for regulated workflows.
03
Customer data is never used to train models or improve shared systems.
04
Prompts, sources, outputs, reviewers, edits, and approvals can be captured in the workflow.
Investors


