Every review, proven genuine.
Anyone can post stars on a lead-gen site. Behind every published review here is a real, verified person — checked by software and confirmed by a human before it ever appears. Here is exactly how.
Software and people, each doing what it is best at
What machines do well
Instant, tireless, applied to every single submission.
- Runs multiple fraud checks on every review — IP and network analysis, keystroke and behavioral biometrics, and proof of a real experience.
- Cross-checks identity against public registries — NPI for clinicians, state license databases, work email domains — in seconds.
- Flags suspected-fake patterns, duplicates, personal information, and defamation risk the moment a review is written.
What humans do well
Judgment on the things software cannot settle.
- Read documents a machine cannot vouch for — pay stubs, W-2s, employer letters, facility move paperwork.
- Weigh each flagged review in context and decide whether it is genuine, disputed, or has to come down.
- Make the final call. No review is published automatically — a person approves every one.
Several independent checks, on every review
A fake review has to beat all of these at once — not just one. Each signal is weak on its own and hard to fake in combination.
Proof of a real experience
A review has to be tied to a genuine connection to the community — move-in or move-out paperwork, billing records, or employment. No experience, no published review.
IP & network analysis
We inspect the network behind each submission to catch review farms, VPN and proxy masking, and clusters of ratings engineered to move a score.
Keystroke & behavioral biometrics
How a review is typed betrays a bot or a copy-paste script. We analyze keystroke rhythm and on-page behavior to separate real writing from automated spam.
Identity & registry checks
Clinicians are matched to the NPI registry, staff to license and employer records, families to their own paperwork — so the person is who they claim to be.
Duplicate & pattern detection
Repeated text, coordinated campaigns, and the tell-tale shape of manufactured praise or targeted attacks are flagged for a human to judge.
Human moderation
Everything the software surfaces lands with a trained moderator, who makes the final publish-or-remove decision. No fake clears both.
From written to published, in four steps
Prove who you are
Before a review counts, the writer establishes their standing — family, current or former staff, or a visiting professional. Automated lookups confirm what they can; anything else goes to a person.
Automated screening
The moment a review is submitted, it runs through heuristics that flag suspected-fake language, duplicates, personal information, and defamation risk — before a human ever opens it.
Human moderation
A moderator reviews the submission and every flag it raised, then decides: publish, hold for more detail, or remove. Nothing skips this step.
Published and marked
Cleared reviews go live and are counted as verified on the community profile — so families can see, at a glance, how many voices behind a rating were actually confirmed.
Different voices, different evidence
Verified is not the same as public
Verification and anonymity are separate choices. A reviewer can appear as “Verified Hospice RN” or stay fully anonymous — either way, we have confirmed who they are behind the scenes. The evidence they provide is stored privately and never shown on their review. Anonymity protects the person; it never weakens the proof.
And to be plain about the limits: verification confirms that a real, qualified person stands behind a review — not that every opinion in it is correct. Reviews we find to be fake are removed and repeat offenders are blocked. A facility can respond to any review, but no facility can pay us to remove or bury a genuine one — we take zero commissions, so nothing bends the record. What we guarantee is that you are reading real people, not manufactured praise.