From search to strategy: UW-Madison's AI tool is rewiring how research collaborations start
UW-Madison's Office of Business Engagement and the Data Science Institute launched the Research and Business-Bridging Intelligence Tool (RABBIT) in January 2025. The goal is simple: match faculty expertise with the right industry partners and cross-campus collaborators fast-and with more accuracy than email chains and guesswork.
As funding tightens and grant criteria lean harder on applied science and partnerships, connection is becoming as important as discovery. This is the gap RABBIT is built to close.
What RABBIT does
RABBIT is an AI-powered faculty discovery platform focused on fit, not keywords. It pulls from publications, grants, patents and industry partnership data to surface researchers who've already proven traction in a specific area.
"Those faculty that are already actively partnering with industry or have patents or grants in the specific research areas that companies are interested in are in high demand. We built RABBIT to meet that need," said Sara Braas, Associate Director at the Office of Business Engagement.
Why it matters
UW-Madison ranks among the top U.S. universities for research expenditures, which makes tracking expertise across a growing campus a moving target. Grant data helps cut through noise: competitive, peer-reviewed awards signal active lines of work where demand already exists.
"Federal grants increasingly require applied science and industry partnerships," Braas said. "They are also focused on grand challenges that require multidisciplinary solutions. RABBIT allows us to build internal, cross-campus teams that can jointly apply for these grants thereby strengthening UW-Madison's competitiveness."
For context on research spending benchmarks, see the National Science Foundation's HERD survey data here.
How it works
The engine uses semantic search to understand intent and meaning, not just terms. You don't need the perfect jargon to find the right person.
"A great feature of the AI-powered semantic search that RABBIT uses is that it helps you find people even if you aren't familiar with the technical terminology," said Kyle Cranmer, Director of the Data Science Institute and RISE-AI. "You can even drag-and-drop a scientific paper that you might not understand and RABBIT will perform the search based on the concepts in that paper."
Behind the scenes, the data is organized around the faculty member as the central profile.
"All of their related information, such as publications, grants and industry collaborations, was linked to that profile," said Jason Lo, Developer at the Data Science Institute. "This gave the system a complete view of each person in one place, making it easier to see connections across their work and understand how different activities relate to each other."
Why build in-house
The team evaluated commercial tools, but they primarily reported what companies were working on, not who on campus could engage on a problem.
"We were looking for a thorough 'who on campus' tool," Braas said. "In the end we hold that data internally, and it was a matter of building a system that could allow us to access it in a more convenient way."
Before vs. after
Before RABBIT, outreach leaned on Google, lab sites and personal networks. That meant more cold emails and higher odds of mismatches.
Now, searches lean on proof of work-funded grants, publications, patents and prior industry activity-so the first call is more likely to be the right call.
Practical ways researchers and R&D partners can use RABBIT
- Assemble multidisciplinary teams around a grant topic by filtering for PIs and co-PIs with funding in adjacent areas.
- Identify campus experts who already have patents or prior industry collaborations in your target domain.
- Upload a technical paper to find conceptually aligned faculty, even if the terminology is unfamiliar.
- Shortlist collaborators with current, active lines of work instead of relying on outdated bios or alumni pages.
- Reduce discovery time for SBIR/STTR, center-scale and cross-agency proposals by starting with proven track records.
What's next
The team is developing an AI-driven query refinement assistant to guide users toward clearer requests and tighter matches. Expect ongoing improvements in how new data sources are integrated and how results are ranked.
For a broader look at how AI is being applied in scientific work, explore AI for Science & Research.
Bottom line
RABBIT shifts faculty discovery from manual search to evidence-based matching. If you build teams, pursue applied grants or scout partnerships, this tool shortens the distance between an idea and the right collaborator.
To learn more about the institute behind the platform, visit the UW-Madison Data Science Institute website.
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