Anyone can send a photograph to an image model and call what comes back an analysis, or a simulation. It is usually confident, specific and wrong — which is worse than useless when your clinic's name is on it. This page is what the platform does instead, in enough detail to be checked.
Four things stand between a photograph and an indicator a clinician would be comfortable standing behind.
A single pass at an image model is not a measurement; it is one opinion with a confident voice. Each of the eleven indicators is read across several independent passes and the score is the middle of them, so one unusual answer cannot move the result. The spread is kept too — an indicator the passes disagreed about is treated differently from one they all agreed on. Below a minimum number of usable passes there is no analysis at all, rather than a confident-looking one built from a single sample.
Each indicator is judged independently, and any one of them may come back unavailable with a short reason — glasses covering the under-eye area, hair across the forehead, lighting too flat to read texture. The rest of the face is still analysed normally.
A circle drawn on someone's cheek is a much stronger claim than a score. It says there, that one, look. So before any marker appears, two things must hold: a second independent read must have placed a marker in the same place, and the photograph's own pixels must support it — a spot marked as pigment has to actually be darker than the skin around it, a blemish has to be redder. That pixel test applies to the indicators drawn as individual points and lines, which is where a wrong mark does the most damage.
When the passes disagree about where something is, the overlay is omitted rather than placed somewhere plausible. A marker in the wrong place is worse than no marker at all, because it looks deliberate — and a client who spots one obvious mistake stops believing the other ten indicators too.
A simulation is only persuasive if the face in it is unmistakably theirs. Four mechanisms, each solving a specific way that image models fail.
Every simulation is built through a constrained prompt with a preservation block that cannot be edited away. Facial structure, jawline, chin, cheeks, nose, eyes, lips and proportions are held; so are age, ethnicity, hair, expression, lighting, background, camera angle and framing.
Where a treatment affects one area, the engine does not regenerate the photograph. It locates the region, crops it, gives the model far more pixels on that area than a whole-face edit ever could, renders only that, re-aligns it, and composites it back through a feathered mask. Everything outside the region is the original photograph, pixel for pixel — it cannot drift, because it was never regenerated.
Image models drift a few percent in zoom and offset, which is enough to make a comparison slider visibly jump. The engine estimates the shift by correlating greyscale thumbnails, applies the correction at full resolution, and refuses corrections above a threshold rather than faking them. Before and after line up, so the comparison is a comparison.
The floor: the result must always be an improvement — never duller, redder, older or worse in any way. The ceiling: show only what a skilled clinician could realistically achieve for this person in one reasonable course of treatment.
These are not gaps waiting to be filled in a later version. They are the boundary that makes the platform safe for a clinic to put its name on, and they are enforced in the product rather than left to the copy.
It does not name a conditionIt reports how skin appears in a photograph. Naming what something is belongs to a qualified practitioner who can examine the person in front of them.
It does not say whether someone is a candidateNothing about suitability, what might rule a treatment out, or how a device should be set. None of it is visible in a photograph, and all of it is the practitioner's call.
It does not discuss medicationNot by name, not by strength, and not as a routine that behaves like one.
It does not classify skin typeA photograph is not a reliable basis for it, and a wrong classification propagates into every recommendation that follows.
It does not promise an outcomeSimulations are illustrative visualisations, not predictions. They are labelled as such wherever they appear, including on anything a client saves or shares.
It does not suggest what you do not offerThe menu is yours, and the client-facing suggestions are filtered to it. The one exception is general skincare advice — a routine, or daily sun protection — which can appear without being something you sell.
Asked explicitly, in plain language, before a photograph is sent anywhere for analysis.
Face photographs are removed automatically once the retention period has passed. The job that does it is checked on every release, because a retention promise nobody verifies is not a promise.
A saved result opens only through a signed link, and those pages are never indexed by search engines.
Full detail in the privacy policy.
Fifteen minutes, including the parts where it declines to answer.