Companies with R&D questions but no partner structure
Teams that need academic or research support but are not yet clear about the business problem, field data, or delivery responsibility.
Industry-Academia
Industry-academia AI R&D collaboration
In-Stars converts FortuneTek industry-government-academia experience into executable collaboration design among enterprises, universities, research centers, and grant programs.
Discuss collaboration and R&D planning
Who This Is For
When enterprise needs and research capability must meet
If the topic is already important but the scope, evidence, owner, or budget decision is still unclear, In-Stars can help organize the first practical path.
Research teams seeking practical validation
University or research partners that need a company field, data boundary, and implementation context for applied AI or DX topics.
Grant or PoC teams needing role clarity
Teams preparing SBIR, SIIR, CITD, or PoC collaboration where partner roles, evidence, and IP expectations must be discussed early.
Consultation Flow
From research interest to an executable collaboration plan
In-Stars converts FortuneTek industry-government-academia experience into executable collaboration design among enterprises, universities, research centers, and grant programs.
Collaboration brief
Translate the business problem into a short brief with objective, field, data, constraints, partner needs, and expected outcome.
Partner-fit review
Match research method, talent, facility, domain knowledge, schedule, and project leadership before asking for a formal proposal.
Field validation design
Define where the work will be tested, what data can be used, who reviews results, and what evidence should remain after the project.
Delivery and confidentiality boundary
Clarify milestone, acceptance criteria, publication limits, IP expectations, and confidential material before execution.
Decision Prep
From research interest to a practical collaboration plan
Industry-academia work should start with a practical business brief, then move into partner selection, validation design, and evidence management.
Business challenge and validation field
Prepare the industry pain point, target field, available users or equipment, and why an academic partner is needed.
Data, IP, and confidentiality limits
Clarify what data can be shared, which materials are confidential, and whether publication or patent issues may appear.
Expected partner profile
Describe the research capability, student/team role, experiment method, and schedule that would make the collaboration practical.
Outputs
A collaboration brief that clarifies roles and evidence
The first output is a decision package: what to do next, what evidence is missing, who should own it, and whether the topic is ready for a proposal, grant, training plan, or PoC.
Collaboration requirement brief
A customer-readable brief that explains problem, field, data, partner role, acceptance criteria, and next meeting agenda.
Partner evaluation criteria
A short matrix for research capability, project fit, communication style, schedule, and delivery responsibility.
Validation and evidence plan
A practical plan for what will be tested, what evidence will be collected, and how the result can support a proposal or PoC.
Timeline And Boundaries
A realistic path for industry-academia validation
Collaboration topic review
Clarify the business problem, field, data, and partner need.
Partner shortlist and validation design
Prepare partner criteria, discussion agenda, and a first validation plan.
Milestone and evidence management
Use milestones and evidence checks to keep the collaboration useful for business decisions.
Consultation Prep
What to prepare before an industry-academia consultation
Before the first call, prepare the business problem, possible validation field, available data, confidentiality limits, expected partner type, schedule, and desired output.
Business challenge and validation field
Prepare the industry pain point, target field, available users or equipment, and why an academic partner is needed.
Data, IP, and confidentiality limits
Clarify what data can be shared, which materials are confidential, and whether publication or patent issues may appear.
Expected partner profile
Describe the research capability, student/team role, experiment method, and schedule that would make the collaboration practical.
Turn the topic into an actionable next step
Share the current business topic and expected outcome. In-Stars will help choose the right next step across AI consulting, grant assessment, industry-academia collaboration, Japan DX (Digital Transformation), or PoC planning.