Grants AI MVP
An Academia case study • 2025
Grants AI emerged from an
internal AI hackathon.
The concept was greenlit for initial development with a focus on supporting our most engaged and high-value user segment: Academics.
How Academics apply for grants today
Academics are often responsible for funding all or some portion of their research. The proposal writing process can require 100+ hours of work. Everything from writing to compiling the details needed for the proposal.
Then, multiply that by 6*, which is a low-end estimate for how many grants a researcher might need to submit within a year.
*Based on a 2023 GrantStation survey where 64% of respondents applied to 6 or grants for the previous year
Finding relevant grants
Funding can come from grants offered by Federal, non-profit, or private funders. Finding these grants requires searching a number of individual databases and piecing together other resources like listservs, professional organizations, and word of mouth. In the words of one of our users, “I am always actively looking.”
Writing grant proposals
Applying for a funding opportunity requires a customized proposal that fits unique content and formatting requirements. For each submission, a researcher must source requirements, write the proposal, and supply supplementary information. The proposal writing process is broadly consistent, but requires detailed knowledge at each step.
How the Grants AI tool works
Our Grants AI solution is built as two separate tools.
AI is behind every stage of the tool. It powers the personalized search experience and streamlines the tedious aspects of the proposal writing process.
Opportunity Finder helps discover relevant funding opportunities.
Proposal Writer focuses on crafting a perfectly customized proposal.
The MVP Grants AI tool flow
The product’s beta focused on creating an MVP end-to-end flow to test product-market fit.
Onboarding question flow
The experience starts with an onboarding question flow. Users are asked basic questions about their role, experience level, and research. Some of these questions are for our own edification, and some are used when we search for matches. Most importantly, the flow ends at an upload page where users provide a past research proposal. This proposal is the basis for the entire experience.
Onboarding did not exist in the original proof of concept. It was introduced to make the tool onboarding more self-service for our early beta users.
Proposal Writer
All funding proposals require some level of customization. Sometimes the alterations can be small, like aligning the timeline and scope of work to the particulars of the award. Some funding opportunities dictate a specific methodology or outcome that requires a more substantive proposal rework. This is where the Proposal Writer’s value lies: it can help a researcher instantly understand the level of modifications expected and generate the corresponding content.
Design updates for MVP
Original outline page in proof of concept
Grant Opportunity Finder
The Grant Opportunity Finder uses a person’s uploaded grant proposal to search for matching grant opportunities. Matches are scored based on relevancy. Included for each result is a description, an explanation for why the grant is relevant to their research, and a hint of what modifications would be needed to make their proposal ready for submission.
Original Writer page in proof of concept
Design updates for MVP
Proposal outline
A new grant proposal begins with the outline.
The Grants tool has expertise about what is required by major funders and understands the requirements for each funding opportunity. Funders can have specific section requirements, word count limits, and formatting requirements.
Original results page in proof of concept
Design updates for MVP
The inner workings of the Grants tool
Users upload a previous research proposal and it is used as the basis for finding new funding opportunities.
We use a combination of vector and web search to find funding matches relevant to the research in the uploaded proposal.
Our system scores each match and surfaces reasoning and explanation for any modifications needed within the original proposal for submission.
Based on the new grant’s requirements, we generate an outline with all sections required for submission.
The first draft of the new proposal can be generated for each section using the original proposal content as the foundation.
In writing mode, users can self-edit or request changes to the generated content using the AI Grant Assistant.
Beta tester pool
I joined the team after the initial proof of concept was developed during an internal AI hackathon. Our first project goal was to determine if we had product-market fit with our MVP. The beta testing period lasted 1 quarter.
We had 326 U.S. users during our public beta
Our first 50 users were invited and had white-glove onboarding via live demos.
Once we added a marketing landing page and self-serve onboarding flow, we opened up access to 20% of Academia’s US users. This resulted in 116 new users.
When we expanded access to ~75% of US users, we gained another 160 users.
Finding product-market fit
We used qualitative and quantitative research to assess the product-market fit of the Grants tool MVP.
NPS survey
When surveyed, 84% of beta users said they would be very disappointed or somewhat disappointed if the tool did not exist.
Opportunity finder engagement
The weekly email that delivered new grant matches saw strong engagement with a 14.2% CTR.
Quality of proposal content
The content generated for new proposals was rated 4 or 5 stars by 93% of users, suggesting strong alignment with user expectations and needs.
Proposal writer engagement
During the beta timeframe we saw the percentage of users generating 2+ sections rise from 19% to 40%.
What happened next
At the end of the quarter the status of the Grants tool was evaluated based on our beta learnings.
Our first iteration of the Grants tool successfully demonstrated our user base was interested in an AI-powered Grant discovery experience.
The original team was greenlit to continue work on Grants; additional resources were also added. Areas of focus for the following quarter were:
Expanding our grant sources and further improving match relevancy
Focusing on user acquisition and monetization testing