Peec AI: LLM Citation Tracking for B2B Brands

What Is Peec AI?

Peec AI is a brand monitoring tool that tracks how frequently your brand appears in responses generated by major AI models including ChatGPT, Perplexity, Claude, and Gemini. It operates by running systematic query probes — buyer-relevant questions and category queries — across these models and recording whether and how your brand appears in the generated answers.

Think of Peec AI as the closest equivalent to a rank tracker, but for AI-generated responses rather than Google search results. A traditional rank tracker checks where your page ranks for a keyword. Peec AI checks whether your brand appears when an AI system is asked a buyer-relevant question in your category.

The tool exists because AEO and GEO work is otherwise invisible. There's no standard analytics channel that shows you AI citation traffic. Buyers who find your brand via an AI recommendation typically arrive at your site as direct traffic or branded search. Without a tool that probes AI systems directly, you have no way to know whether your brand is appearing in buyer research sessions or whether your AEO investments are producing results.

What Peec AI Measures

Peec AI tracks four primary categories of data:

Citation Frequency

How often your brand appears per a defined set of query probes run across AI models. If Peec AI runs 50 queries across ChatGPT, Perplexity, and Claude and your brand appears in 12 of the generated responses, your citation frequency for that query set is 24%. This is the primary performance metric for AEO and GEO programs.

Citation frequency is tracked over time, so you can see whether it's growing, stable, or declining. Changes in citation frequency following specific content interventions — a new Reddit thread campaign, a structured data update, a review platform push — tell you which activities are moving the needle.

Share of Voice

Your brand's citation rate relative to category competitors. If your brand appears in 24% of probed queries and your nearest competitor appears in 35%, your AI share of voice is lower. If you've been at 8% and you're now at 24%, your share of voice has grown meaningfully regardless of what competitors are doing.

Share of voice is the competitive framing that makes citation frequency contextually meaningful. A 24% citation rate is excellent in a competitive category where the leader has 28%. It's weak in a category where competitors average 60%. Peec AI tracks both your absolute citation rate and your position within the competitive set.

Sentiment of Mentions

When your brand is cited, what does the AI say about it? Peec AI categorizes the sentiment and characterization of citations: positive recommendations ("Brand X is widely used for its low false-positive rate"), neutral inclusions in comparison lists, or negative or skeptical mentions. The sentiment dimension matters because a brand that gets cited negatively — consistently characterized as having reliability issues or poor support — may be worse off than a brand that isn't cited at all.

Sentiment analysis also identifies specific claims AI systems make about your brand, which tells you what the aggregated community and third-party sources have taught the models to believe. If AI systems consistently cite an outdated product limitation, that's actionable: it tells you where your community and content signals need updating.

Query Coverage

Which buyer questions and query types surface your brand, and which don't. A brand might have strong citation rates for direct recommendation queries ("what's the best X for Y?") but weak rates for comparison queries ("compare X, Y, and Z platforms") or exploratory queries ("what are the main options for X?"). Understanding query coverage gaps tells you which content and community work needs to be done to expand citation presence across the full range of buyer research patterns.

Why Peec AI Matters for AEO and GEO Work

You can't optimize what you can't measure. This is the foundational case for a tool like Peec AI in any AEO or GEO program.

Without systematic measurement, AEO work is directional but unvalidated. You know Reddit matters for LLM citations. You know structured data helps. You know third-party mentions contribute. But you don't know whether the specific work you're doing in a specific category is changing citation behavior for your brand. You're flying without instruments.

Peec AI provides the instrument panel. It answers: Is your citation rate changing? Is competitor share of voice moving? Are specific query types showing improvement? Are the characterizations getting more specific and positive over time? With this data, AEO programs become iterative and evidence-based rather than activity-based.

The measurement also serves a practical business function: demonstrating AEO and GEO ROI to stakeholders who aren't seeing it in standard marketing analytics. When AI-referred buyers arrive as direct traffic with no referrer attribution, pipeline contribution from AEO work is invisible to CMOs looking at the analytics dashboard. Peec AI data — showing rising citation frequency correlating with rising direct traffic and branded search volume — makes the invisible influence visible.

How Nerativ Uses Peec AI

Peec AI is Nerativ's primary measurement tool for all AEO and GEO client programs. The workflow has three phases:

Baseline Measurement

At the start of every engagement, Nerativ runs a baseline measurement using Peec AI. We define a query set of 20-50 buyer queries that represent how buyers in the client's category research their decision — direct recommendation queries, comparison queries, use-case queries, problem-statement queries. We run these across ChatGPT, Perplexity, and Claude and record the baseline citation frequency and competitor share of voice. This establishes the starting point against which all subsequent progress is measured.

Weekly Probing

Throughout the engagement, Peec AI runs weekly query probes across the defined query set. We track changes in citation frequency, watch for new citation appearances following content interventions, and monitor competitor movement. The weekly cadence is important: AI model behavior can shift after training updates or as new community content gets indexed, and weekly tracking catches those changes in near-real-time rather than discovering them in a monthly report.

Monthly Share of Voice Reports

Monthly client reports use Peec AI data as the primary performance metric. We report citation frequency trend over the past 30 days, share of voice position relative to the 3-5 key competitors we track, notable shifts in query coverage, and any changes in citation sentiment or characterization. These reports connect AEO activity — what content was created, what community work was done — to measurable changes in AI citation behavior.

Manual Testing as a Complement

For teams that aren't yet using a dedicated tool, manual testing is a starting point. Pick 20-30 queries that represent buyer research in your category. Run each one across ChatGPT, Perplexity, and Claude. Record which queries return your brand, what the characterization is, and which competitors appear. Do this monthly with the same query set. Over time, the pattern tells you whether your AEO work is moving the needle.

Manual testing has clear limitations: it's time-consuming, inconsistent, and produces no automated trending data. It also can't scale to the query volume needed for a comprehensive view of share of voice. Peec AI automates all of this and adds the competitive benchmarking that makes individual citation data meaningful.