01
Scope
Atlas measures how often selected AI assistants name businesses in response to a fixed prompt set for a city and category. It does not measure service quality, market share, search traffic, or buyer conversion.
- Observation
- One assistant response
- Scan run
- Prompt × assistant × repetition
- Snapshot
- One published ranking dataset
02
Sampling
Prompts describe buyer situations, constraints, research questions, and selection decisions. Each prompt is sent independently to every assistant in scope and repeated according to the prompt-set protocol.
Crisis
Immediate event
Advice
Whether to act
Situation
Specific circumstances
Constraints
Budget or access
Learning
Process question
Decision
Who to consider
Validation
Check a named firm
Follow-up
Later conversation turn
Bucket names and counts are versioned. Selected prompt wording is published in dated vertical research reports so readers can inspect the context behind a recommendation; the complete operational panel remains restricted.
03
Ranking inclusion
Ranking layer
Prompts that request a business recommendation or evaluation. Citations from these responses determine rank and response mention rate.
Editorial layer
Advice, crisis, learning, or constraint prompts that do not request names. Samples may be published, but they are excluded from ranking calculations.
04
Entity extraction
- 1Parse business and professional names from each response.
- 2Match names to Atlas records using normalized names, aliases, domains, and canonical entity links.
- 3Create an unverified citation-only record when no existing entity matches.
- 4Store the citation with its response, assistant, prompt key, and snapshot source.
Directories and non-business reference sources are excluded from firm rankings.
05
Metrics
Mentions
Eligible responses that named the business.
Response mention rate
mentions ÷ eligible responses. Reported with a 95% Wilson confidence interval.
Mention share
business mentions ÷ all business mentions in the snapshot.
Assistant coverage
Number of tested assistants that named the business at least once.
Rank orders businesses within one snapshot. Comparisons across categories require the underlying rates and sample sizes.
06
Models and snapshots
Claude
anthropic/claude-sonnet-4.6
ChatGPT
openai/gpt-4o
Gemini
google/gemini-2.5-flash
Perplexity
perplexity/sonar
Snapshots retain their prompt-set version, model set, source runs, sample size, and publication date. Published snapshots are not rewritten. A model or prompt-set change starts a new comparison series.
Model change · August 3, 2026
Gemini changed from gemini-2.5-pro to gemini-2.5-flash; Perplexity changed from sonar-pro to sonar. Results on opposite sides of this change are not directly comparable.
07
Prompt sets
Active sets are shown with their published bucket counts. Expand a set for details.
No active prompt sets.
08
Access and corrections
Public
- Rankings and confidence intervals
- Per-assistant counts
- Prompt-set versions
- Professional citation tables
Research access
- Response-level records
- Per-prompt breakdowns
- Historical exports
- Custom scan scopes
For data access, factual corrections, or removal requests, email support@viclaro.app. Factual corrections include names, websites, entity matches, and category assignments. Ranking positions are not changed on request.
Atlas and per-brand audits
Atlas reports category-level results from its versioned prompt sets. A per-brand audit runs the same assistant panel — ChatGPT, Claude, Gemini, Perplexity — against a narrower deck of buyer prompts scoped to a single subject brand, and reports two measures side by side: the observed prompt-level citation rate (how often the panel names the brand across the deck) and a page-side citability prediction (a model-scored estimate of the ceiling on citation the audited page content alone could support). A divergence between the two measures is consistent with constraints beyond page quality — off-page authority, retrieval behavior, category positioning, brand familiarity, and sample variance are each candidate contributors. Draft answer blocks generated for uncited prompts are editorial starting points and require fact and compliance validation before publishing. Results across audits are comparable only when the prompt panel and model definitions match; the deck is held constant across successive audits of the same brand so that change between audits is attributable to shipped content or off-page shifts rather than measurement drift.