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Sample Proof

See exactly what a completed Proof Package looks like

This gallery shows how a completed proof package explains confidence, uncertainty, manual checks, and screenshot evidence without exposing any real customer or target.

Start private searchSee Pricing

The samples below use mocked data to protect real user privacy.

See how the proof package explains why a match is strong

The sample package now previews the same evidence factors users should apply in the live results route so confidence language stays consistent before and after purchase.

  • Confidence framework

    Each result is explained through multiple signals such as photo similarity, platform fit, location overlap, and freshness rather than a face match alone.

  • Context and metadata

    The sample package shows the app source, visible profile details, and timing cues that help separate strong proof from weaker leads.

  • Visual proof and uncertainty notes

    Screenshot review is paired with notes about low-confidence, stale-profile, and no-match outcomes so buyers know how to read the evidence responsibly.

  • Review-before-inclusion

    The sample explains that customer-facing evidence should appear only after OopsBusted review, with contributor-assisted or internal manual capture kept inside the same visible-evidence boundary.

Confidence model

A sample report, read end to end

Sample profile portrait — synthetic example content, labelled SAMPLE

Megan, 23SAMPLE

Los Angeles · ages 20–30

High confidence

  • Tinder

    SAMPLE — example Tinder profile screenshot, synthetic contentSAMPLE

    Captured 3 days ago

  • Bumble

    SAMPLE — example Bumble profile screenshot, synthetic contentSAMPLE

    Captured 5 days ago

  • Hinge

    SAMPLE — example Hinge profile screenshot, synthetic contentSAMPLE

    Captured 2 days ago

Human reviewed
Yes — before delivery
Freshness
Captured within 3 days
Signals aligned
Photo, platform fit, city
Still uncertain
Bio overlap is thin on one source

Synthetic sample content. Not a real person, not a confirmed identity, and not proof of intent or activity.

Open a screenshot detail page

The five signals behind a confidence label

  1. Photo similarityPrimary signal

    Facial structure, repeated selfie angles, and other visible features should line up across the source photo and the unlocked profile screenshots.

  2. Platform fitContext signal

    A result gets stronger when the surfaced app matches the likely platform, age band, or behavior pattern you already expected.

  3. Location overlapSupporting signal

    City, travel pattern, and distance cues add weight when they align with what you already know about the person you’re searching for.

  4. Prompt and bio similarityIdentity signal

    Repeated phrasing, prompt themes, job clues, and profile habits can strengthen identification beyond the face alone.

  5. Recentness or activityFreshness signal

    Activity notes and screenshot timing help separate a current lead from a stale, paused, or deleted profile.

How to read a confidence label

Live results and this sample page use the same three labels, and each one comes with instructions for what to do next.

  • High confidence

    Multiple signals align

    Use this label when face match, context, and screenshot sequence support the same conclusion instead of relying on one familiar-looking image.

    What to do: Review the strongest detail page first, then keep the proof private and contextual.

  • Medium confidence

    Promising, but still incomplete

    This usually means the face or metadata looks plausible, but one or more supporting signals remain thin, missing, or slightly inconsistent.

    What to do: Open the detail view and compare multiple screenshots before deciding whether the lead is usable.

  • Low confidence

    Lead, not conclusion

    Low-confidence results should be treated as hints that may justify more careful review, not as proof that the person definitely has an active profile.

    What to do: Do not let a weak match outrun the evidence that supports it.

How weak, no-match, and ambiguous outcomes should be read

Low-confidence, no-match, and false-positive outcomes each need a different reading. This is the guidance the live results route applies.

Low confidenceWeak results still need restraint

A weak match usually means one or more confidence factors did not line up strongly enough to treat the result as decisive proof.

  • Outdated or filtered photos can resemble the person you’re searching for while still being the wrong person.
  • One screenshot without matching metadata is weaker than a full profile sequence.
  • Use low-confidence results as a reason to review carefully, not to escalate immediately.
No matchNo visible match is still an interpretable outcome

A clean search does not prove the person has never used a dating app. It means the workflow did not surface a reviewable match with enough visible evidence right now.

  • The profile may be deleted, hidden, paused, or outside the currently visible search surface.
  • Poor source photos and missing optional clues can reduce the chance of a confident match.
  • Treat no-match outcomes as inconclusive rather than as final innocence or final guilt.
False positivesStale profiles and recycled photos create ambiguity

Some cases stay ambiguous because profile images are old, screenshots are thin, or the account changed after the user relationship timeline shifted.

  • Old photos can make a real account look current when it no longer is.
  • Deleted profiles remove context that would otherwise strengthen or weaken the case.
  • The safest interpretation comes from multiple aligned signals, not from urgency alone.

Ready to run your own?

You review a preview of the strongest candidates before choosing what to unlock.

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