Artificial intelligence for science parks, incubators, and accelerators
The park team's own files, and a workspace each resident company can run without buying an AI stack of its own.
Request a consult for your park or program.
Where a park actually needs an assistant
Resident companies are small. The park is the organization that holds the regulations, the landlord file, and the program. That is the archive worth making askable.
Member questions
Admission rules, lab access, IP policy, and rent terms are answered from the current document, the same way for every company that asks.
Programs and cohorts
Mentors and program managers draft briefs from the application, the last review, and the curriculum, instead of starting over each cohort.
Shared services
Facilities, safety, and the landlord's obligations live in one assistant the front desk can actually use.
A workspace per company
A resident team gets its own assistants and its own budget. One company's files are not retrieved for another.
Reporting to the sponsor
The quarterly note to the ministry, the university, or the investor is assembled from the numbers and the narratives you already have.
Cost that does not surprise the park
Dozens of small teams can join. You pay for the ones doing heavy work, and you can see who.
The pattern these organizations sit inside
There is no serious survey of science parks alone. The relevant finding is about knowledge work across organizations, which is what a park's staff and its companies do.
of organizations in McKinsey's 2025 global survey use AI in at least one business function, up from 78% a year earlier
McKinsey, The State of AI, 2025
management is now among the functions where respondents most often report AI use, beside IT and sales
McKinsey, The State of AI, 2025
organizations are still experimenting or piloting. Reported enterprise-wide profit impact remains uncommon
McKinsey, The State of AI, 2025
is the practical constraint for a park: many teams, one procurement, a cap on each team
How Convex is sold — not an industry survey
Figures come from the published surveys and case reports named on each card. They describe those samples, not a promise of the same result.
What Convex puts in front of the team
The park keeps one deployment. Each company keeps its own documents and its own spend.
Several models
Pick the model for the task from several leading models, in one place.
Specialized assistants
An assistant for member services, and a separate workspace pattern for the companies inside the park.
A personal assistant
Each person has an assistant tuned to how they work.
Workspace assistants
Shared assistants that keep the company's context for the whole team.
Search over your documents
Answers come from your own procedures, contracts, and records, with the source attached.
Documents out
Word files and images today. CSV export is planned.
A cap per person
Token use is visible per person, and the spend can be exported.
Adoption dashboard
See where AI is actually being used, and where it is not.
Cloud, or entirely on your own infrastructure
The same product. The difference is where the data sits.
Cloud
What is available today, hosted.
- Access to several leading models
- Specialized, personal, and workspace assistants
- Search over the company's documents
- Word documents and images
- Token-use tracking and reports
- Adoption dashboard
On-premise
Full control inside your environment, including a network with no outbound connection.
Everything in Cloud, plus:
- Your own model endpoints
- Offline licensing and payment for a disconnected site
- Assistants and workflows built for your process
Start with the member handbook
If the front desk can answer from it with a citation, the rest of the program is the same shape.