Grant opportunity matching cover

Grant opportunity matching

Eligibility and collaboration requirements are explicit alongside topic fit.

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For
University research support offices
Solves
Researchers miss suitable calls or pursue ineligible ones.
Delivers
Research funding shortlist
Built in
about 4 weeks of creation time, MVP in 4 days
Investment
$7,500 for the MVP, $28,500 for the full product
Run it
Inside your business, or as part of your offer to clients
01

What it does

For university research support offices, turn published funding calls and researcher-declared project profiles into research funding shortlist.

  1. Match research themes.
  2. Extract eligibility.
  3. Compare institution constraints.
  4. Identify partner requirements.
  5. Track call revisions.
  6. Save pursuit decisions.

What goes in, what comes out

What the customer puts in
  • Published funding calls
  • Researcher-declared project profiles

AI drafts, people review. Transparent opportunity matching and shortlist platform.

What the customer gets
  • Research funding shortlist
02

How it works

The workflow

  1. In
    Start with

    Published funding calls and researcher-declared project profiles

  2. 1

    Define buyer-selected criteria

  3. 2

    Gather authorized opportunity information

  4. 3

    Apply explicit eligibility rules

  5. 4

    Propose matches with evidence

  6. 5

    Let the user review uncertain conditions

  7. 6

    Save a shortlist and track the resulting conversations or applications

  8. Out
    Finish with

    Research funding shortlist

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Editable criteria, dated sources, eligibility evidence, missing-data flags, saved shortlists, rejection reasons, deadline alerts and owner follow-up.

Integrations and data access

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.

03

How we build it

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

  1. 1

    Scoping call

    Day 1

    Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

  2. 2

    MVP

    4 days

    One buyer segment, one recurring use case; first modules: match research themes; extract eligibility. Manual review in the loop. Built by our AI software factory.

  3. 3

    Paid pilot

    5 days

    Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

  4. 4

    Full product

    9 days

    Remaining modules: identify partner requirements; track call revisions; save pursuit decisions. Self-serve onboarding, billing, monitoring and the wider integration set.

  5. 5

    Run and improve

    Monthly

    We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

  1. Pick the riskiest assumption. Here: will university research support offices use it to solve "researchers miss suitable calls or pursue ineligible ones"?
  2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
  3. Run a paid pilot. Produce a small shortlist and ask the buyer to verify fit independently.
  4. Measure, then decide. Track eligible match rate and useful opportunities. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. 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.

What the build depends on. Current source information, explicit eligibility rules, entity identity checks and inspectable fit reasoning. Sparse evidence limits match quality.

04

Investment

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

  1. Phase 1

    MVP

    One buyer segment, one recurring use case; first modules: match research themes; extract eligibility. Manual review in the loop.

    $7,500 · about 4 days of creation time

  2. Phase 2

    Paid pilot

    Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

    $9,000 · about 5 days of creation time

  3. Phase 3

    Full product

    Remaining modules: identify partner requirements; track call revisions; save pursuit decisions. Self-serve onboarding, billing, monitoring and the wider integration set.

    $12,000 · about 9 days of creation time

Indicative total, MVP to full product$28,500about 4 weeks of creation time · start with the MVP from $7,500

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

StageHosting and infrastructureAI usageTotal per month
MVP and paid pilotabout 3 customers$30–$60$40–$90$70–$150
Full productabout 50 customers$110–$210$280–$560$390–$770
05

Run it or resell it

Internally

For your own team

University research support offices run it inside the business: published funding calls and researcher-declared project profiles in, research funding shortlist out, reviewed by your people.

For your clients

As part of your offer

Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.

Your brand, or this one

Run it under your own brand, or start from this concept style.

  • primary#912747
  • accent#54c9c1
  • surface#f1e4e8
  • ink#22201e
Headings
Playfair Display
Text
Source Sans 3
Voice
Rigorous, transparent, cited
Selling it to your own clients: the go-to-market playbook

Pricing 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.

Message to test

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.

Where to find buyers

Research administration networks

Lead magnet

A documented funding shortlist for one research group

The first 30 days

  1. 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.
  2. Week 2: prepare this demonstration using authorized or synthetic material: a documented funding shortlist for one research group.
  3. Week 3: present it through research administration networks and seek one narrowly scoped paid pilot.
  4. Week 4: review eligible match rate, useful opportunities, total delivery effort and a concrete renewal decision before increasing scope.

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.

Success metrics

Eligible match rate, useful opportunities

Retention and expansion

Refresh opportunity profiles, improve criteria from accepted and rejected matches, and offer deeper verification for shortlisted options.

Why clients would pick it

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.

Alternatives and positioning

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.

Main delivery costs

Source collection, profile updates, entity resolution, eligibility verification, analyst research and customer feedback review.

06

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.

Get this solution built

Built for you by our AI software factory, MVP in about 4 days. Tell us about your business and how you want to run it: inside your company, or as part of what you offer your clients. We reply within one working day.

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