Extraction before judgement
The page is read and its structure, copy, actions and proof are extracted first. Findings are formed from that record rather than from an impression.
AI-assisted
An automated review is only useful if you can check it. FixSift analyses your page and attaches the evidence to every finding, so you can judge the verdict rather than trust it.
What you get: your FixSift score and your highest-priority findings, with the evidence behind each one. See what FixSift checks.
Enter your address and FixSift returns your score and your highest-priority findings. It is the same scanner the homepage runs — there is no separate engine behind this page.
This page reads the FixSift scan through one lens: an automated analysis that cites the evidence behind each finding. The scanner is the same one the homepage uses — what changes is which findings are brought to the front.
The page is read and its structure, copy, actions and proof are extracted first. Findings are formed from that record rather than from an impression.
Each finding carries what triggered it — the line, the element, the absence — so you can disagree with it specifically.
Low-confidence observations are marked as such instead of being presented with the same certainty as the obvious ones.
Every scan records the analysis and scoring versions that produced it, so results stay comparable over time.
The same page scanned twice should produce substantially the same findings. Where it does not, that is treated as a defect.
The problem with automated audits is not that they are automated. It is that they are unfalsifiable: a list of generic recommendations with nothing tying them to your actual page.
FixSift is built the other way round. The analysis has to point at something on the page, and anything it cannot support, it does not claim.
That also sets the honest limits. It reads what is publicly visible; it does not know your customers, your margins or your funnel data, and it does not pretend to.
"This headline does not name an audience" is arguable. "This headline — your exact line, quoted — does not name an audience" is checkable.
Where no proof exists near the primary action, the finding says so rather than inventing a weakness in the proof.
Where the page is ambiguous, FixSift says the reading is uncertain instead of guessing confidently.
Recommendations that would apply to any website are the clearest sign an audit never really read the page.
An automated audit that describes elements your page does not have is worse than no audit.
If the model changes silently between runs, you cannot tell improvement from drift.
Any finding without evidence is an opinion. It may be right, but you cannot act on it with confidence.
No account, no installation, no card.
Messaging, conversion, trust and clarity.
A score and your highest-priority findings.
Every page on this site runs the same scanner and the same analysis. There is no separate engine behind any of them — what changes is which findings are explained first.
The analysis is AI-assisted: the page is extracted and evaluated programmatically, and language models are used for the parts that require reading comprehension. Every finding carries the evidence it came from, and the versions used are recorded with the scan.
Findings have to reference something extracted from the page. Observations that cannot be supported are dropped or marked low-confidence rather than presented as fact.
No. It does the consistent, repeatable part quickly and cheaply. Judgement about your market, your pricing and your customers stays with you.
They should. Consistency between runs is treated as a quality requirement, and rescans are compared finding by finding so you can see exactly what changed.
One address, one scan, one prioritised list. Free, and you keep the link to your result.