Methodology

How our reports work: what we check, how scores are computed and why you can trust them.

The 9-channel audit

An AI analyst walks through public sources the way a meticulous customer or a hired consultant would: searches for the company, opens its profiles, reads the reviews. Then it scores each of the 9 channels and builds a 90-day action plan.

What exactly gets checked

  • Search results for the company name and branded queries
  • Social profiles: activity, presentation, posting cadence
  • Messengers and contact channels: Telegram, WhatsApp Business
  • Maps and directories: Google Business Profile, industry listings
  • Review platforms: Google Reviews, Trustpilot, G2, Yelp
  • Mentions in the press, blogs, forums and by influencers

For every channel we save 1–3 working proof links.

The 0 to 10 scale

  • 0: No trace of the channel found at all
  • 1–2: Minimal: a profile exists but is empty or abandoned
  • 3–4: Low and fragmented: something exists but isn't maintained
  • 5–6: Average: alive and acceptable, with room to improve
  • 7–8: Strong: active, well-kept, delivering results
  • 9–10: Industry-leading

The score combines presence and completeness, cadence, execution quality, officialness, discoverability and how feedback is handled.

Overall rating

The average of 9 channel scores, times 10: an average of 4.3 gives a 43% rating. No hidden weights.

Honesty rules

  • No invented facts: when nothing is found, the report says "not found" instead of making something up.
  • Every finding is grounded in public sources; the important ones come with proof links.
  • An empty profile doesn't count as presence: it scores 1–2 and lands in the risks.
  • Small businesses typically score 3–6 per channel. That's normal: the report exists to help you grow, not to flatter you.

AI Visibility: how we probe the models

We ask each of the 8 models three questions, phrased the way real users phrase them. The answers are parsed into a structured scorecard: what the model knows, who it recommends and who it confuses you with.

  1. Knowledge probe: "What do you know about company X?" Checks whether the model knows the brand and what exactly it says about it.
  2. Category probe: "Recommend the best companies in category Y." Shows whether you make the list, and at what rank.
  3. Direct probe: "Would you recommend company X?" An honest assessment: strengths, weaknesses and which alternatives the model suggests instead of you.

What we measure

  • Share of voice: The share of category probes where AI mentioned your brand, aggregated across all models.
  • Confusion risk: Low, medium or high: whether a model mixes you up with same-name companies and attributes their facts to you.
  • Visibility score: A combined 0–100% score for brand knowledge and recommendations across all models.

The model roster

  • ChatGPT (OpenAI): internal knowledge
  • Claude (Sonnet 5) (Anthropic): internal knowledge
  • Gemini (3.1 Pro) (Google): internal knowledge
  • Grok (4.3) (xAI): internal knowledge
  • Mistral (Medium 3.5) (Mistral): internal knowledge
  • DeepSeek (V4 Pro) (DeepSeek): internal knowledge
  • Qwen (3.7 Max) (Alibaba): internal knowledge
  • Perplexity (Sonar Pro) (Perplexity): live web search

We run current model versions and refresh the roster as new ones ship. Perplexity answers from a live web search; the rest answer from internal knowledge.