Solution Database / Science and Research
Grant opportunity matching
Researchers miss suitable calls or pursue ineligible ones. Eligibility and collaboration requirements are explicit alongside topic fit.
01The offer
For university research support offices, turn published funding calls and researcher-declared project profiles into research funding shortlist. Address the recurring problem: researchers miss suitable calls or pursue ineligible ones. The value hypothesis is a more complete, reviewable deliverable with less repeated preparation; the pilot must establish whether that benefit is real.
- For
- University research support offices
- Takes in
- Published funding calls and researcher-declared project profiles
- Delivers
- Research funding shortlist
- Message
- Grant opportunity matching for university research support offices. Eligibility and collaboration requirements are explicit alongside topic fit. Demonstrate the claim through a documented funding shortlist for one research group.
- Lead magnet
- A documented funding shortlist for one research group
02How it works
- Match research themes
- Extract eligibility
- Compare institution constraints
- Identify partner requirements
- Track call revisions
- Save pursuit decisions
Workflow
Define buyer-selected criteria, gather authorized opportunity information, apply explicit eligibility rules, propose matches with evidence, let the user review uncertain conditions, save a shortlist and track the resulting conversations or applications. Start with published funding calls and researcher-declared project profiles and finish with research funding shortlist.
AI and people
Extract criteria, normalize opportunity descriptions and explain possible fit. Use explicit rules for hard requirements. Do not invent missing eligibility facts or represent a suggested match as a verified qualification.
Screens
Key screens: Funding watchlist, eligibility evidence, deadline calendar. Open with a filterable opportunity feed and clear fit explanations. Each profile shows source evidence, eligibility conditions and missing information. Keep saved, rejected and needs-review states. Include a deadline or next-action view without hiding the basis of recommendations. In this product, the first view is funding watchlist, followed by eligibility evidence and deadline calendar.
Admin
Editable criteria, dated sources, eligibility evidence, missing-data flags, saved shortlists, rejection reasons, deadline alerts and owner follow-up.
03Market gap
Alternatives buyers use today
Manual research, directories, generic databases, referrals and existing opportunity marketplaces. Differentiate on this specific proposed advantage: eligibility and collaboration requirements are explicit alongside topic fit. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Where this wins
A maintained niche opportunity dataset and documented relevance feedback, supported by relationships with the intended buyer community. For this solution, build around eligibility and collaboration requirements are explicit alongside topic fit. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
04Why now
Science and Research teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: researchers miss suitable calls or pursue ineligible ones.
05Proof & signals
Channels where buyers gather: Research administration networks. Metrics that prove it works: Eligible match rate, useful opportunities.
Paid pilot
Produce a small shortlist and ask the buyer to verify fit independently. Record why each option is accepted or rejected and whether it leads to a useful next step. Review missed eligible options too. For this solution, use published funding calls and researcher-declared project profiles and evaluate research funding shortlist. Agree success thresholds with the buyer before starting; collect a baseline for eligible match rate, useful opportunities. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
06Execution plan
MVP
Begin with university research support offices and one recurring use case. Build the first two modules: match research themes; extract eligibility. Provide operator assistance for the third module: compare institution constraints. Deliver research funding shortlist through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
First 30 days
Week 1: interview five prospective buyers in this segment: university research support offices. Ask to see a recent example of the problem and their current process. Week 2: prepare this demonstration using authorized or synthetic material: a documented funding shortlist for one research group. Week 3: present it through research administration networks and seek one narrowly scoped paid pilot. Week 4: review eligible match rate, useful opportunities, total delivery effort and a concrete renewal decision before increasing scope.
After the pilot
After paid pilots establish value, automate the remaining modules: identify partner requirements; track call revisions; save pursuit decisions. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.
Retention
Refresh opportunity profiles, improve criteria from accepted and rejected matches, and offer deeper verification for shortlisted options.
Integrations
Authorized datasets, papers, protocols, code and research records. Permitted opportunity feeds, customer profiles, calendars and CRM exports. Keep initial outreach or applications as user-reviewed drafts. These are candidate integration categories, not verified supported connectors.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: match research themes; extract eligibility. Manual review in the loop. | 4 days | $8,000 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 5 days | $9,000 |
| Full product | Remaining modules: identify partner requirements; track call revisions; save pursuit decisions. Self-serve onboarding, billing, monitoring and the wider integration set. | 9 days | $12,500 |
| Total | $29,500 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $40–$90 | $70–$150 |
| Full product (about 50 customers) | $110–$210 | $280–$560 | $390–$770 |
Revenue model to test
Test USD 150-600 monthly for one narrow opportunity feed, or USD 750-2,500 for a bespoke researched shortlist. Price manual verification and custom research explicitly. These are pricing hypotheses.
Cost drivers
Source collection, profile updates, entity resolution, eligibility verification, analyst research and customer feedback review.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
Take it further
Concept proposal expanded from the 315-solution conversation. Demand, pricing, differentiation, build scope and integration feasibility are hypotheses, not verified market findings. Category link is inspiration rather than evidence of business viability.