The Omni AI visibility engine is a query workflow: it takes a list of customer questions, asks them across several AI models, parses every answer for your business and your competitors, and records the results so trends and gaps are visible. The bottom line: it turns an unmeasurable question, does AI recommend you, into a share of runs you can track month to month, and we state its main limit plainly, which is that automated answers are a proxy for what people see in the apps.
What it does, step by step
- Reads the question list. The customer questions for a business, with place and situation, kept in a sheet.
- Asks each question across models. Parallel API calls to several AI platforms.
- Parses each answer. Code checks for the target company by name and by URL or domain patterns, and records other businesses and sources.
- Writes the results. One record per question, model, and date: whether the company was named or cited, its position, and who else appeared.
- Runs on a schedule. The same questions again, so results can be compared over time.
What it produces
| Output | What it tells you |
|---|---|
| Citation and mention frequency by model and question | Where you are visible and where you are not |
| Competitive mapping | Which questions competitors win |
| Source list | Which sites the answers draw on, so you know where to build presence |
| Content gap list | Questions where no page of yours matches |
| Week over week or month over month trend | Whether changes are working |
How it relates to the free scan and the plans
Our free scan is a readiness check: eight weighted checks of one page for schema, server rendered content, meta tags, content density, robots.txt access for seven search bots, llms.txt, headings, and answer ready content. The engine is the outcome side. Our plans run each customer's monitored questions, 10, 25, or 50 a month depending on the plan, across ChatGPT, Gemini, Perplexity, Claude, and Google AI, and report which questions named the business and which competitors appeared.
What it cannot do
- It cannot see private answers. Automated calls are a proxy for what people see in the consumer apps, so we spot check against them.
- It cannot remove variation. SparkToro and Gumshoe found less than a 1 in 100 chance that two AI responses returned the same brand list, so we count shares across repeated runs and do not report single answers.
- It cannot guarantee outcomes. It measures what AI systems say. It does not control it.
How to use a workflow like this yourself
The design is not proprietary. An n8n workflow with a schedule, a sheet of questions, API calls with web search, a parsing code node, and a results table does the same job, and packaged tools do it for you. What matters is the discipline: fixed questions, enough runs, honest reporting of noise, and using the source list to decide where to build.
Questions people ask
What is the Omni AI visibility engine?
A query workflow that asks a business's customer questions across several AI models, parses each answer for the business and its competitors, and records results so trends and gaps are visible.
How is the engine different from the free scan?
The scan checks readiness of a page. The engine measures outcomes: whether AI answers actually name the business.
Which platforms does monitoring cover in the plans?
ChatGPT, Gemini, Perplexity, Claude, and Google AI, with 10, 25, or 50 customer questions monitored a month depending on the plan.
What are the limits of automated monitoring?
Automated answers are a proxy for the consumer apps, results vary between runs, and monitoring measures what AI says without controlling it.