Methodology

Most restaurant guides are built from one viewpoint: a critic, a network of inspectors, or the raw volume of user reviews on a single platform. Each of those signals, on its own, can be wrong. A critic visits once. Inspectors miss neighbourhood rooms. Review platforms reward popularity over cooking.

Guidavera was built to resolve that. We combine independent signals, verify each listing by hand, and publish a Consensus Score that reflects how a restaurant is regarded across the full picture, not one opinion within it.

Guidavera aggregates from several independent categories of signal:

• Authoritative international guides — recognition from established restaurant authorities whose awards carry weight in the industry.
• Professional critics and press — long-form reviews and features from trained food writers.
• Local food publications — editorial coverage from city-specific publications with meaningful editorial standards.
• Diner review platforms — large-scale review volume, used as a secondary signal after editorial recognition.
• Restaurant-verified data — hours, menus, contact details, and photos sourced directly from the restaurant and confirmed against their public channels.

We do not disclose exact weightings or the full list of individual sources. That opacity is deliberate: published weights invite gaming, and our process is intended to surface genuine quality, not reward optimisation.

Guidavera is built to keep getting better. The data we hold, the signals we draw on, and the way we weigh them are all in continuous improvement. A restaurant’s rank today is a snapshot, not a verdict for all time.

• Rankings shift as signals arrive. A new critic review, a fresh accolade, a price change, a shift in diner sentiment, or a confirmed closure all feed back into the score. Restaurants move up and down as the picture changes.
• The algorithm itself improves. We refine how we weigh sources, surface signal, and account for volume bias as we learn more about what genuine quality looks like across cuisines, neighbourhoods, and formats.
• New inputs broaden the picture. As we add new sources, finer-grained attributes, and richer on-the-ground data, the consensus sharpens. Restaurants that were previously underrepresented become visible, and ones that relied on a single popularity signal recede.

A consensus guide that never moved would mean either the world had stopped changing or we had stopped paying attention. Neither is true.