SHEKARI · SERVICES
Track brand presence in conversational LLM answers
This audit samples how conversational AI systems respond to a defined set of buyer questions: whether your brand is named, how it is described and which alternatives appear beside it. We record prompts, model, date and conditions rather than turning a few responses into a universal ranking claim. If the goal is to track a specific source URL in an AI search result, the AI Search Visibility audit is the relevant service.
When is this service relevant?
Typical situations worth investigating before choosing the next step.
Responsibilities overlap with adjacent services. AI Search Visibility focuses on identifiable search features; this service samples language-model conversations.
The execution decision can be based on insufficient evidence. We review purchase questions, comparison prompts, dated responses, brand aliases and platform limitations.
Findings may be overstated beyond the conditions of observation. Results from one account or model cannot be generalized to all users or providers.
Scope and potential deliverables
The final scope is agreed after reviewing the website; these are typical work areas.
A bounded, versioned set of realistic buying and comparison prompts in the target language.
A dated response matrix recording model/product, brand mentions and named alternatives.
A brand-description accuracy review and public-source corrections, separate from AI search URL visibility.
A repeatable sampling protocol explaining answer variation and why a small sample is not market share.
How the engagement works
The steps are adjusted to the scope we agree on.
- 01
Confirm the audience and scope: Brands that want an evidence-based starting point across multiple assistant experiences.
- 02
Gather evidence and establish the baseline: We review purchase questions, comparison prompts, dated responses, brand aliases and platform limitations.
- 03
Separate the assignment from neighboring services: AI Search Visibility focuses on identifiable search features; this service samples language-model conversations.
- 04
Prepare implementation tasks and deliverables: A question/model/date matrix, direct and indirect mention labels and a factual-correction backlog.
- 05
Review findings and monitor valid indicators: Repeat observations under recorded conditions, compare coverage and inspect actual source links when present.
IN-DEPTH GUIDE
What is involved in LLM visibility audit?
Useful details to consider before deciding on this service.
Build a fixed set of customer questions and competitors, record the model and observed responses, label the role of brand mentions, then compare future samples under comparable conditions. This is an answer-level brand audit, not a page-index ranking report.
Separate models and interfaces
Output changes across model versions, chat products and retrieval modes. Record the interface and access conditions for each sample.
Build a real customer-question set
Use buyer comparisons, provider selection and specialist questions derived from actual needs rather than prompts containing only your brand name.
Avoid leading prompts
Brand-seeded prompts can inflate mention rates. Keep unprompted discovery questions separate from branded questions.
Label the kind of appearance
Distinguish simple naming, detailed descriptions, factual errors and explicit recommendations. They do not represent the same business outcome.
Track change over time responsibly
Repeated samples can reveal directional changes while noting differences in model behavior and platform functionality.
Check the underlying brand facts
Compare outdated or mistaken descriptions with verifiable public sources before deciding what to correct on owned pages.
Do not invent a universal visibility score
A proportion from a small prompt set is not worldwide LLM market share. Always publish the sample boundary beside the figure.
Deliver a reusable evidence pack
Provide the logged answers, assumptions, prioritized factual improvements and an explicit plan for the next comparable sample.
START A CONVERSATION
Request an LLM brand-visibility audit
Define the brand, relevant competitors and buyer prompts for a dated, bounded sampling audit.
FAQ
Frequently asked questions
A few useful details before we begin.
Is brand visibility in an LLM chat the same as Google search visibility?
Output changes across model versions, chat products and retrieval modes. Record the interface and access conditions for each sample.
How is a useful prompt bank assembled for LLM visibility?
Use buyer comparisons, provider selection and specialist questions derived from actual needs rather than prompts containing only your brand name.
Why do prompt wording changes matter in the measurement?
Brand-seeded prompts can inflate mention rates. Keep unprompted discovery questions separate from branded questions.
How do you classify a brand appearance in an AI answer?
Distinguish simple naming, detailed descriptions, factual errors and explicit recommendations. They do not represent the same business outcome.
Can shifts in LLM visibility be treated as definitive trends?
Repeated samples can reveal directional changes while noting differences in model behavior and platform functionality.
What practical decision should the LLM visibility report support?
Provide the logged answers, assumptions, prioritized factual improvements and an explicit plan for the next comparable sample.