1. Remove incompatible options
The platform must be active, available in the selected market, appropriate for adults and compatible with the stated intent and audience environment.
FBF methodology
How FBF decides which dating apps deserve your attention.
FBF starts with basic eligibility, then compares the factors that can change whether a platform fits your stated goal. The system uses broad fit bands rather than fake precision.
2. Weigh the fit factors
FBF compares intent, geography, privacy and safety, relationship-style support, value, product quality and freshness.
3. Explain the result
Each recommendation should tell you why it appears, what could make it a weaker choice and when important facts were last checked.
Eligibility first
FBF does not score obviously incompatible platforms.
Hard filters prevent the ranking system from making an irrelevant platform look attractive simply because it performs well in another category.
Available where you are looking
The current platform record must support the selected market.
Compatible with what you want
A platform should support the stated dating intention instead of requiring the user to fight the product environment.
Relevant environment
FBF removes options that are incompatible with the audience preference selected in Finder.
Initial fit model
The current weighting is a hypothesis, not a universal truth.
The weights are configurable and should evolve only with real product evidence. Public fit must remain understandable and defensible.
Intent compatibility
The largest initial factor because a dating platform should first support what the user actually wants.
Geographic suitability
A strong product is not useful if it is not viable in the selected market.
Privacy + safety
Visibility, verification and risk-reduction controls matter in an adult-dating decision.
Relationship-style support
Relationship and preference environments should be compatible with the user's stated needs.
Price / value
Free functionality and paid-feature trade-offs affect whether an app deserves time and money.
Product quality
Usability and product friction influence whether a theoretically compatible platform works in practice.
Data freshness
Current information gets more confidence than stale platform data.
Affiliate payout
Commercial payout has zero weight in the public Fit Score.
Why no 93.7% match?
Precision should be earned, not decorated onto a card.
FBF uses Excellent Fit, Strong Fit and Good Fit until enough real-world outcome data exists to justify calibrated numeric probabilities. A decimal number would look sophisticated while communicating more certainty than the data supports.
A
Excellent Fit
Strong compatibility across the highest-priority dimensions.
B
Strong Fit
Good alignment with meaningful trade-offs worth understanding.
C
Good Fit
Compatible enough to consider, but not the strongest current option.
How does FBF handle changing prices and features?
Reviews and platform records carry checked dates and primary-source links where practical. Material changes should enter a freshness queue instead of silently remaining “current.”
Can sponsorship buy a better recommendation?
No. Sponsored placements, if introduced later, must be labeled and cannot purchase an Excellent Fit label or change the public Fit Score.
Does FBF use AI to invent platform facts?
No. The blueprint requires platform facts and hard eligibility to come from structured records and verified sources. AI can help explain information, not fabricate it.
What if FBF gets something wrong?
Material corrections should be reviewed, updated and handled through the editorial correction process rather than hidden.
See the methodology in action
Use the Finder and get an explainable shortlist.
The output should show enough reasoning for you to disagree with it intelligently.