What your score
actually means.

Core Web Vitals (LCP, CLS, FID), page load time, image optimization, caching, and third-party script weight.

Measured using Google PageSpeed Insights. Scores reflect real-world load performance against current web standards.

ARIA labels, image alt text, color contrast ratios, keyboard navigation support, and focus management.

Evaluated against WCAG 2.1 AA criteria using both automated checks and structural HTML analysis.

HTTPS enforcement, HTTP security headers (Content Security Policy, HSTS, X-Frame-Options), and mixed content warnings.

Inspected directly from response headers. A padlock icon alone is not enough: missing headers lower the score.

Meta titles, descriptions, Open Graph tags, canonical URLs, structured data, heading hierarchy, and internal link health.

Parsed from the page HTML. Each missing or malformed element reduces the score. Structured data is validated against schema conventions.

Viewport configuration, touch target sizing, and mobile-friendliness signals from the page markup.

Checked using Google PageSpeed Insights mobile scoring alongside direct viewport and tap target inspection.

HTML validity, deprecated elements, inline script and style ratios, and common markup errors that affect rendering.

Analyzed through direct HTML inspection. Excess inline scripts, deprecated tags, and structural errors each reduce the score.

Whether analytics tools are present, and basic privacy compliance indicators like cookie consent signals.

Detected from page scripts and markup. Pages with no measurement setup score low: you cannot improve what you do not track.

Navigation structure, form complexity, layout clarity, and interaction design patterns.

Evaluated by AI analysis of the page structure, navigation depth, and form design. Scores reflect how easily a visitor can accomplish what they came to do.

Tone (professional, casual, playful), voice uniformity across sections, and whether the personality in the headline carries through to the body copy.

AI analysis reads all visible text on the page and flags tonal inconsistencies or sections where the voice shifts without reason.

Whether a first-time visitor can understand what the product does and why it matters within the first few seconds of reading.

AI analysis evaluates the above-the-fold content specifically. Vague, buried, or jargon-heavy value props score low.

Testimonials, customer logos, review counts, credentials, and security badges, and how convincingly each is placed.

AI analysis identifies trust elements and assesses their placement, specificity, and persuasiveness. Generic or hard-to-find trust signals reduce the score.

Headline impact, CTA clarity, benefit-driven language vs. feature-listing, jargon density, and emotional resonance.

AI analysis evaluates each copy element. Copy that sells outcomes scores higher than copy that describes features.

Missing content types that high-converting pages typically include: FAQ sections, case studies, pricing transparency, blog presence, and about pages.

AI analysis compares present content against a baseline of what pages at this stage of the funnel should include, then flags what is absent.

Whether language complexity, vocabulary, and tone match the likely target audience.

AI analysis infers the intended audience from the content and checks whether the reading level, terminology, and style actually match them.

User journey from landing to conversion: friction points, unclear next steps, missing CTAs, and navigation that pulls visitors away.

AI analysis traces the intended path through the page. Every dead end, missing CTA, or confusing next step is flagged and factored into the score.

Whether the messaging articulates a clear differentiator or defaults to generic claims any competitor could make.

AI analysis flags phrases and value claims that are industry-standard boilerplate versus those that establish a specific, ownable position.

Primary, secondary, and accent colors in use, whether the palette is intentional and consistent, and whether one-off colors dilute the brand.

Colors are extracted from the live page CSS and screenshots. A disciplined palette scores higher than one with many one-off colors.

Font families in use (serif, sans-serif, monospace), typographic hierarchy clarity, and whether the type scale is consistent across headings, body, and captions.

AI analysis inspects the CSS font stack and evaluates hierarchy, weight variety, and scale consistency against modern typography best practices.

Spacing consistency across margins and padding, visual weight distribution, and whether a coherent scale creates rhythm throughout the layout.

AI analysis evaluates the layout for cramped or overly spacious areas and checks whether spacing decisions follow a consistent scale.

Whether brand colors are harmonious (complementary, analogous, or triadic relationships), and text-to-background contrast ratios.

Color relationships are assessed against color theory principles. Contrast ratios are checked against WCAG AA and AAA thresholds.

Whether multiple fonts complement each other, excessive font family count, and text alignment consistency across sections.

AI analysis checks font pairing logic and flags mismatched combinations, too many families, or inconsistent alignment patterns.

Reading path clarity, CTA prominence, whitespace used to group related content, and whether the above-the-fold area communicates purpose immediately.

AI analysis evaluates whether font sizes, colors, and spacing work together to guide the eye and establish clear importance levels.