Proever · Tokyo, Japan · AI Product + Experience · 2016–2017
Designing an AI knowledge Product in 2016
Proever asked whether artificial intelligence could preserve and make useful the knowledge, talent, and expertise of current and former employees for the people who came next.
August 2016–December 2017
90-second case
- Problem
- Enterprise knowledge disappeared when expertise remained tacit, projects ended, or employees left; documents alone did not preserve the people and context behind decisions.
- Mandate
- Work under contract as Director of Product Management/Design, integrating Product definition, experience architecture, and founder-vision translation.
- Decision
- Center the Product on people and expertise—not a document store alone.
- What happened
- The Product and Design work spanned the company’s pre-sales build, commercial-sales start, and early corporate transition in 2016–2017.
- Why it matters now
- Its strategic value is its age: the work predates the current generative-AI cycle while already connecting Product, Experience, organizational knowledge, and human expertise.
My role
How I contributed.
Working under contract as Director of Product Management/Design, I integrated Product definition, Experience architecture, and founder-vision translation.
I partnered with the founder and technical collaborators; I was not a cofounder or employee.
Current reconstruction
The core mechanism.
A present-day visual of the system described in the case.
Judgment under constraint
Decisions that shaped the work.
Decision
Frame the Product around people, expertise, and future use—not content accumulation alone.
- Why it was hard
- A knowledge Product can capture more material without helping an employee find the right context, understand why it matters, or reach the person who holds tacit knowledge.
- My judgment
- Connect Product definition and Experience architecture around the organizational knowledge problem and the founder's AI-enabled vision.
- Consequence
- The work created an integrated Product and Design direction across the company’s pre-sales build, commercial-sales start, and early post-launch period.
Historical work · 2016–2017
The Product treated enterprise knowledge as a relationship among people, expertise, and future work.
Proever was a Tokyo-based startup working on enterprise knowledge and talent management using AI. Its central premise was to preserve knowledge, talent, and expertise from current and former employees for future employees. The Product emphasized people and expertise rather than documents alone.
Jessie was contracted by the founder as Director of Product Management/Design, integrating Product definition, Experience architecture, and founder-vision translation. She was not a cofounder or employee.
- 01Organizational knowledge
Current + former employees · projects · expertise
- 02Product model
People and knowledge relationships · future use
- 03Experience
Find expertise · understand context · connect to a person
- 04Learning
Contribution · correction · retained organizational memory
Period context
The chronology matters more than a modernized label.
My tenure ran from August 2016 through December 2017. ProEver, Inc. was established in Tokyo in November 2015, commercial sales of its knowledge and talent-management system began in February 2017, and the company was absorbed into Management Solutions on October 31, 2017.
That chronology places the work across a pre-sales build, commercial-sales start, and corporate transition.
What I now see in it · retrospective
Enterprise knowledge is still a context, trust, permission, and human-responsibility problem.
I now see the historical Product problem as larger than enterprise search. Useful knowledge has ownership, access, freshness, provenance, conflict, and a route to human expertise. Modern AI can synthesize and retrieve more powerfully, but it also makes those boundaries more important.
The 2016–2017 technology predated modern LLMs, agents, and contemporary evaluation. The current interpretation explains why the Product problem endured.
Historical work
AI-enabled knowledge + talent premise
Preserve and make useful the expertise of current and former employees.
Present interpretation
Governed knowledge system
Evidence, permissions, provenance, conflict, human expertise, and task outcome become one Product system.