How CitationWorks Works

We monitor a curated list of the most influential large language models, including ChatGPT, Claude, Perplexity, Gemini, and other systems that shape how buyers discover and compare products.

Our system runs a broad set of prompts against those models, covering category questions, brand comparisons, buying scenarios, use cases, and the kinds of queries a prospect would naturally ask before they visit your website.

The dashboard shows where your brand appears, how often it appears, how competitors are positioned, and whether the response is accurate, incomplete, or misleading.

Every project also gets a dedicated human expert. That expert calibrates the analysis for your category, checks whether the outputs match your actual positioning, and separates signal from noise so you are not reacting to raw model chatter.

Because our team has experience in both marketing and software engineering, the recommendations are grounded in how brands are communicated and how AI systems actually behave.

You get continuous monitoring instead of one-off snapshots, historical data instead of isolated checks, and reporting that stays comparable over time.

That means you can see when your brand disappears from answers, when messaging drifts, when a competitor starts getting recommended first, and whether those changes are temporary or part of a broader trend.

We turn that into clear reporting, practical recommendations, and a repeatable workflow your team can actually use.

We are model-agnostic and report what major AI systems actually say. We do not promise to control their outputs, and we do not pretend to see inside proprietary training data.

Our job is to give you an honest view of reality, explain what appears to be driving it, and recommend the next best actions based on observable outputs and known best practices.