Core Concepts

:::summarybox remember What AI visibility is, and how it differs from search The six nouns Trakkr is built around Why your brand appears (or doesn't) in AI answers A map to where each idea lives in the product

When AI doesn't name you, you're invisible to a slice of buyers you'll never measure with web analytics. That's the gap Trakkr fills.

The vocabulary

Six nouns appear on nearly every screen. Once you know these, everything else hangs off them.

:::conceptgrid cols=3 brand|Brand|What you track. nike.com is a brand; so is its parent, Nike, Inc. prompts|Prompt|A question worth tracking. "Best running shoe for marathons?" model|Model|An AI engine. Trakkr polls eight of them - ChatGPT, Claude, Gemini, Perplexity, AI Overviews, Grok, Meta AI, DeepSeek. mention|Mention|Your brand name appearing inside an AI answer. Graded by position, sentiment, and context. citations|Citation|A URL the AI cites as a source. The clearest lever you have for changing what models say. competitors|Competitor|Another brand you track alongside yours. Powers every head-to-head view.

note

One process knits them together: a research run asks every prompt to every model and captures every mention and citation that comes back. Trakkr does one of these every day.

Why you appear (or don't)

When someone asks Perplexity for the best CRM, three things decide whether your brand is named.

1. Training data presence

Models learn from the open internet - Wikipedia, news, reviews, well-linked blogs. If your brand showed up in quality sources during training, the model knows you exist. Paid ads, most social posts, and gated content don't make it in.

2. Contextual relevance

Knowing you exist isn't enough. The model has to connect your brand to the right category. "An all-in-one workspace" is harder to place than "a note-taking app for teams." Clarity wins.

3. Sentiment and positioning

How you're described matters as much as whether you're named. If the sources AI trusts position you as a leader, that follows you into the answer. If they position you as the alternative-to-X, that follows too.

These three are what every Trakkr feature ultimately moves. Citations is the most direct lever - change what models read about you, and the answer changes.

The eight models we track

Each model has different training data, a different cutoff date, and a different way of pulling in fresh information. Scoring 80% on Claude and 30% on Perplexity is normal - and both numbers are useful signals.

Type	How it answers	Examples
Pure training data	Frozen at training time	Claude, stock ChatGPT, Meta AI
Live retrieval	Searches the web first, then writes	Perplexity, AI Overviews, ChatGPT Search
Hybrid	Mixes both	Gemini, Grok, DeepSeek

There's no single number that summarizes everything - and that's the point. Different models reach different audiences, and an improvement plan usually targets one tier at a time.

The visibility loop

Improving AI visibility isn't a one-time audit. It's a loop, and Trakkr is built around it.

Measure with Research and the Dashboard - establish a baseline. Understand with Citations and Perception - see what's driving the numbers. Act with Content, Optimize, AI Pages, and Actions - change what the model sees. Re-measure the next day. Live-retrieval models (Perplexity, AI Overviews) move fast. Training-data models take longer.

The map - where each idea lives

The rest of the docs are organized by what you want to do. Use this as a routing index.

If you want to…	Start here
See your numbers at a glance	Dashboard
Pick the right questions to track	Prompts → Research
Know where AI learns about you	Citations
Compare against rivals	Competitors
Know how AI describes your brand	Perception
Find content opportunities	Content
Make your site easier for AI to read	Optimize Site, AI Pages
Automate alerts and reports	Workflows, Integrations

Going deeper

Three reference pages cover the long-tail detail. They're worth bookmarking, not reading top-to-bottom.