AI Search Visibility

DataWise runs a persistent panel of brand- and category-related queries against each supported LLM on a recurring schedule, parses the answers, and records every mention of your tracked entity along with the sources the model cited. Unlike a one-shot prompt, the tracker builds a time series, so you can see not just whether you appear in a given answer but how your mention share is trending week over week across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.

AI Search Tracker is query-centric: you pick a set of prompts (for example, 'best project management tools' or 'how to compare CRM vendors') and watch which brands get mentioned over time, which gives you the full competitive landscape for a topic. Brand Tracker is entity-centric: you lock in a single brand and monitor everything LLMs say about it across a broader query set, which is what you want for your own company, a client, or a key competitor. Most teams use both: Search Tracker to map the category, Brand Tracker to monitor specific entities.

The tracker covers the five engines that matter most for AI-first search: ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Perplexity, and Google's AI Overviews feature inside classic Search. You can filter any dashboard to a single platform with one click, which is useful when a specific engine is underperforming or over-indexing for your brand. Additional engines get added as their APIs stabilize; the roadmap is published inside the community.

Traditional keyword volume counts how many people type a query into a search engine in a given month. AI search volume estimates how many times LLMs generated answers to that query, multiplied by an estimated audience reach, which is a better proxy for how many eyeballs your brand actually lands in front of when it gets cited. The metric is still evolving across the industry but is already the closest approximation to 'impressions inside AI answers' that any tool exposes to the average user.

LLMs are trained heavily on high-authority, broadly-indexed sources, so when they cite a mention about your brand they often pull from Wikipedia, YouTube, Reddit, or large publications before they pull from your own site. The citing-domain chart is specifically designed to expose this so you can work the other direction: instead of trying to outrank those sources, you make sure your brand is represented accurately in them. Updating a Wikipedia stub, publishing a YouTube explainer, or earning a mention in a top-tier publication often moves the needle faster than publishing on your own blog.

For steady-state monitoring, weekly sampling is plenty and gives you a clean trendline without wasted credits. After a major event (product launch, rebrand, funding round, crisis, or a big content push) switch to daily for two or three weeks so you can watch the LLMs update in near-real-time. The Brand Tracker dashboards are designed around both cadences: hover over any point in the mentions-over-time chart to see the exact queries and citing domains that drove that week's numbers.

Yes. AI Search Visibility does not ping competitor sites or trigger any outbound signal to the brands you monitor. It queries the LLMs directly and parses the responses, so from the competitor's perspective nothing has happened. This is the same model used by traditional rank trackers and is fully within the acceptable-use policies of every supported engine. Use it to benchmark against direct rivals, emerging threats, or acquisition targets without signalling your interest.