How RubixScout works

From messy grant search to a decision-ready shortlist.

RubixScout turns your project description into structured funding context, searches active grant data, ranks realistic matches, and helps you decide which opportunities deserve your time.

Example request
We are a UK-based SME building AI automation for manufacturers and need non-dilutive innovation funding for a pilot.
Applicant: SME
Sector: AI + manufacturing
Use: pilot
Funding: non-dilutive
5
shortlisted matches
4
AI work modes
1
shareable result page
The flow

Four steps from prompt to application plan.

01
Applicant

Tell RubixScout what you are building

Describe the applicant, country, sector, stage, funding need, and what you want to avoid. The prompt becomes structured search context instead of a loose keyword search.

Example: German AI startup · MVP · manufacturing automation · non-dilutive funding · avoid loans
ApplicantRegionUse of funds
02
Open grants

The database is filtered before AI touches it

RubixScout checks region, status, deadline, themes, applicant type, and eligibility notes first. Bad listings, expired grants, and obvious mismatches are pushed down or removed.

This keeps the shortlist focused before the AI writes anything.
Open grantsEligibilityDeadline
03
Fit score

AI reviews the shortlist like a grant analyst

The agent explains why each opportunity matched, what evidence supports the fit, and what risks still need source verification.

You get a useful decision view, not just a list of links.
Fit scoreWhy matchedRisks
04
Checklist

Turn a match into action

Unlock qualification, research, prep, and draft views to move from ‘interesting grant’ to a practical application plan.

The goal is to help you decide what to apply for and what to ignore.
ChecklistApplication angleDraft support
Why it feels different

Less searching. More deciding.

The product is designed around the real user question: “Should I spend time applying for this?” That means the result page focuses on fit, blockers, source confidence, and next steps.

Normal grant database
RubixScout
Hundreds of vague results
A ranked shortlist
Keyword matching
Context + eligibility matching
You read every source manually
AI summarizes the source and risks
Hard to know what to do next
Prep and draft steps included
Beta focus

Better matches before more features.

The current iteration prioritizes search quality, eligibility signals, email capture, and funnel analytics. New features only matter if the shortlist is worth trusting.

Try your project →