Private AI search
Success Stories
How users found proof without guessing, bluffing, or alerting the target
These case studies are anonymized and reconstructed with mocked data to protect privacy. The names, cities, and identifying details are not real, but the workflow patterns and proof outcomes reflect real use cases.
3
sample cases
Each one represents a common investigation path without exposing a real client identity.
100%
anonymized
Names, locations, and personal details are mocked or blended to protect privacy.
0
target alerts
All examples reflect the same private-first workflow used across the site.
Privacy notice
Why these stories use mocked data
Every case study below uses reconstructed timelines and mocked personal details. The point is to show how the product is used in practice without exposing any real customer or target.
Case studies
Representative stories from the workflow
Each case follows a realistic product path: intake, route selection, likely match review, and a proof package that gives the user a clearer next move.
Case A, 34, West Coast metro
Tinder stayed active after exclusivity
A user wanted clarity after exclusivity had already been discussed, but did not want to create a fake profile or confront with only suspicion.
Intake
Started with one recent selfie and a likely city.
Finding
The focused Tinder route surfaced a likely match without requiring wider bundle coverage.
Proof delivered
- Returned screenshots of a likely Tinder profile match
- Profile context showing the account was still active after the relationship milestone
- A same-day proof package the user could review privately
Outcome
The user stopped second-guessing and made the next relationship decision based on proof instead of assumption.
Case B, 29, Northeast
Hinge came back clean, Bumble surfaced the real lead
The initial suspicion pointed to Hinge, but the first pass did not confirm anything strongly enough to act on.
Intake
Started with a clear portrait and expanded into a broader bundle after the first platform came back clean.
Finding
The bundle route surfaced a stronger Bumble match after the Hinge-specific search did not resolve the case.
Proof delivered
- Clearer cross-platform comparison instead of repeating one-app checks blindly
- Screenshots from the platform that actually matched the suspicion
- A better basis for deciding whether more proof was necessary
Outcome
The user avoided wasting time on the wrong app and moved directly from uncertainty into a clearer answer.
Case C, 37, Southern region
Photo-led search found the overlooked platform
The user suspected mainstream app activity, but the strongest signal was actually on a less obvious platform route.
Intake
Started with a recent face-forward image and broadened scope when the first lead looked too weak.
Finding
The investigation surfaced the likely profile on a platform the user had not considered first.
Proof delivered
- Likely match screenshots organized into one reviewable package
- Evidence strong enough to rule out several wrong assumptions
- A private workflow that avoided notifying the target
Outcome
The user gained clarity faster than manual app hopping and did not need to keep guessing where to look next.
What repeats
Patterns across successful searches
The stories vary, but the strongest outcomes usually follow the same three patterns.
Better first inputs create faster clarity
The strongest cases usually begin with a recent, clear source photo rather than a vague clue or guess.
Focused routes save time when the app is obvious
Users move faster when they start narrow on the most likely platform instead of checking every app at once.
Bundles matter when the first lead is wrong
Some of the best outcomes come from broadening only after the initial platform assumption comes back clean.
Next step
Move from reading proof stories into getting your own answer
If these cases mirror the kind of uncertainty you are dealing with, the next move is to start the intake with the strongest photo and the clearest platform clue you have.
Trust cluster
Use success stories as proof, then move into the route that fits your case
These pages help turn trust into action: technology, privacy safeguards, and the live intake route.
AI Photo Matching
See how the matching workflow works, what affects accuracy, and why it beats manual searching.
See AI matchingTransparency Report
See representative monthly search volume and the safeguards that prevent the target from being alerted.
Read reportSecurity Protocol
Examine the technical safeguards, encryption standards, and data residency protocols.
Review securityEthics & Safety
Understand our operational boundaries and zero-tolerance policy for harassment.
Read ethicsSample Proof Package
See a reconstructed example of the final PDF report delivered to users.
View samplesStart Search
Move directly into the intake flow when the strongest photo and platform clue are ready.
Start search