The REAL Method is RankingLocal.ai's own framework for measuring whether AI engines will recommend a local business. Each of the four letters is a layer scored 0–25, and the four layers add up to a 0–100 AI recommendation rate. It is not an official ranking from Google, OpenAI, Anthropic, Microsoft, or Perplexity — it is the practical scorecard we ship every cold scan and every weekly report against.
A practical recommendation-rate scorecard for local businesses. It answers four questions: can AI systems Recognize you as a real local entity, is there enough Evidence to back a recommendation, can your pages Answer customer questions verbatim, and can AI crawlers reach the Link graph that proves you matter?
AI answer engines weigh different signals than Google. We split the recommendation rate into the four REAL layers so you see exactly where you're weak — Recognize, Evidence, Answer, Link.
Can the model recognize you as a real local business — who you are, what you do, where you serve?
Is there enough proof — reviews, pricing, service detail, named third-party signals — to justify recommending you?
Do your pages give direct, quotable answers AI engines can reuse verbatim in their generated reply?
Can AI crawlers reach the page, parse the HTML, and follow the link graph that backs your authority?
This layer checks whether your business is machine-readable as a real local entity. Without it, AI engines can't tell whether you exist as a business worth recommending.
This layer looks for the proof an AI engine needs before it puts your name in front of a customer. Specific evidence beats generic marketing every time.
This layer checks whether your page gives concise, reusable answers instead of vague marketing copy. AI engines lift answers verbatim — your job is to give them something to lift.
This layer checks whether AI crawlers can access the page at all and follow the link graph behind it. Recognize, Evidence, and Answer don't matter if a crawler can't reach them.
The exact weighting is part of the product. The important part is knowing which categories affect your score and why they matter.
The free checker models how different answer engines tend to respond to local-business signals. These are modeled profiles, not official platform formulas.
Leans heavily on structured entities, clear answers, and trustworthy citations.
entity + answer fitRewards strong local entity signals, schema, and consistency with map-style understanding.
entity + local contextOften benefits from fresh, citation-friendly pages with explicit supporting evidence.
freshness + citationsTends to respond well to transparent language, trust signals, and coherent page structure.
trust + clarityModeled as benefiting from entity clarity, local consistency, and structured summaries.
entity + summariesModeled with stronger sensitivity to multimodal and local-knowledge cues.
local + multimodalModeled with more weight on freshness, real-time context, and visible evidence.
freshness + recencyOur first monitoring release focuses on three stable provider paths before broader engine coverage.
3-engine rollout firstThat is why one site can score better for Perplexity than for Claude, or better for Google AI than for ChatGPT, even when the underlying website is the same.
The free checker profiles all 7 major AI engines based on public ranking-factor research. But our continuous monitoring product only queries three stable, official API paths: Perplexity, Claude, and Google (Custom Search). Here is why.
The free AI Visibility Checker still models 7 engines because it runs once and uses public ranking-factor research, not live answers. That modeling is useful at audit time. For week-over-week monitoring we only sell what we can measure without breaking.
No. The REAL Method is RankingLocal.ai's own evaluation framework for local-business recommendation readiness across answer engines.
Yes. Off-page Evidence, competitor strength, and query intent still matter. The checker measures recommendation readiness, not guaranteed distribution.
The checker estimates readiness from your site against the four REAL layers. Monitoring measures what supported engines actually returned over time, then aggregates those results into a tracked recommendation rate.
We explain the four layers and the categories we test, but we keep the exact weighting internal so the score stays useful as a working product rather than a static checklist you can game.
This page explains the framework. The free checker runs your site through it and tells you which of the four REAL layers is suppressing your AI recommendation rate today.