Security and privacy
Protection of transport, users, data and legal transparency.
Categories: 4
Weight: 22%
METHODOLOGY
Transparent measurements. Versioned rules. Honest limits.
A Verunel report should be reproducible enough to inspect and clear enough to use. Every run records its scope, environment, tool versions, rules, evidence and limitations.
556Catalog checks
22Catalog categories
7Connected pillars
60%Minimum coverage to publish the index
verunel-risk-v2
From checks to an understandable score.
The catalog contains 556 checks in 22 categories. Scope/methodology and inventory explain execution; they have no score and do not affect the index. The other 20 categories form 7 pillars.
Experience and accessibility
How reliably and inclusively people can use the site and its key journeys.
Categories: 3
Weight: 20%
Performance and reliability
Availability, speed, responsiveness and errors during real execution.
Categories: 4
Weight: 20%
SEO and discoverability
Technical signals that help search and answer systems understand the site.
Categories: 2
Weight: 15%
Content and business utility
Offer clarity, trust and the site’s ability to guide a user to an outcome.
Categories: 2
Weight: 10%
Technical foundations
Valid structure, maintainable technologies and platform capabilities.
Categories: 3
Weight: 8%
Environment and change
Environmental estimates and meaningful changes from a comparable prior measurement.
Categories: 2
Weight: 5%
How do we calculate the score and coverage?
An observation contributes: passed 1, warning 0.5, failed 0. A metric's normalized contribution is the mean of usable observations of the same metric and rule × 100, rounded to one decimal place.
Earned points = rule weight × normalized contribution / 100. Pillar score = total earned / total maximum points × 100, rounded to an integer. The index is the weighted mean of scored pillars, rounded again to an integer. Rule weights come from the versioned presentation registry; weight 0 does not affect the score.
Unknown, not tested, blocked and error have no score outcome and reduce coverage; not applicable is excluded. Metric coverage is usable / relevant observations, then weighted by rules and pillars. The index is published only at 60% usable coverage or above.
Coverage describes the inputs to this particular assessment; it does not imply that every catalog check was performed. Assessed URL count is a separate measure. An absent baseline is neither improvement nor regression.
Status is a check outcome. Contribution is a score calculation. Severity describes the finding, priority orders recommended work, and coverage describes usable measurements. These terms are distinct.
Inspect the sample contributions →THE REVIEW LIFECYCLE
Seven traceable steps from scope to comparison.
1. Confirm scope
The chosen domain, page limit, device profile, saved journeys and authorization define the run. The report records the actual reviewed URLs.
2. Collect evidence
Browser, HTTP, performance, accessibility, SEO, privacy, content and runtime signals are captured in a controlled environment.
3. Normalize results
Duplicate observations are connected, source data is preserved and every normalized metric keeps its provenance.
4. Apply versioned rules
Rule IDs, thresholds and scoring formulas are stored with the report so later changes do not rewrite history.
5. Explain and prioritize
Measured facts remain separate from interpretation and recommended action. Confidence and effort are explicit.
6. Freeze the version
The web report and PDF come from the same immutable data cut and receive a stable report ID.
7. Compare carefully
Runs are compared only when scope, device, metric definition and environment are compatible.
Coverage before confidence.
Every score publishes its formula version, weights, thresholds, coverage and missing data. A missing or blocked result is unknown, not zero — and never silently converted into a pass.
- Severity — how serious the observed condition is
- Confidence — how strongly evidence supports the conclusion
- Priority — when the team should act
- Effort — expected implementation difficulty
AI connects evidence into decision context.
The model analyzes patterns across measurements and disciplines, consolidates repetition, explains likely business impact and prepares priorities with concrete recommendations. Every conclusion remains traceable to evidence.
One result, the right next action
Each finding says whether it is ready for implementation, needs confirmation in a user journey or belongs in a separate specialist review. The team immediately sees who should own the work and how to verify the improvement.
See the finished result before you commit.
The sample includes scores, coverage, findings, evidence, recommendations, comparison and a clear record of the exact report version.