Measuring Brand Authority in the Age of AI Chatbots: A Guide to Share of Voice Analytics

The digital discovery landscape has fundamentally shifted. In 2025, your brand is no longer just fighting for blue links on a traditional search engine results page (SERP). It is fighting for space inside AI Overviews, Large Language Model (LLM) answers, and chatbot recommendations. These are the exact surfaces where modern customers now receive "the answer" to their queries, often without ever scrolling further or clicking a traditional link. The challenge facing marketing professionals today is that traditional SEO tools are ill-equipped to track brand visibility within these new conversational interfaces. You might be winning classic rankings and dominating traditional search metrics, yet simultaneously losing mindshare and authority inside the AI-generated answers that increasingly influence purchasing decisions.

This is where Share of Voice (SoV) becomes the critical metric for survival and growth. SoV demonstrates how frequently your brand appears versus competitors across AI-generated answers and classic SERPs. It reveals who actually owns the conversation within your specific industry or category. If you have not yet run a brand visibility audit using LLMs, you are essentially flying blind on the most rapidly growing discovery surfaces. The tools detailed in this guide are designed to bridge this visibility gap, offering specialized tracking for AI Overviews, LLM chatbots, and the hybrid search environments of the near future.

The Evolution of Share of Voice: From SERPs to Chatbots

Historically, Share of Voice was a metric borrowed from public relations and traditional advertising, measuring the percentage of mentions a brand received compared to its competitors within a specific media landscape. In the SEO world, this translated to tracking how often a domain appeared in the top 10 results for a set of target keywords compared to the aggregate visibility of competing domains. However, the rise of generative AI has introduced a layer of complexity to this concept.

In the context of AI chatbots and search engines, Share of Voice is no longer just about ranking positions. It is about presence within the generated text itself. When a user asks ChatGPT, "What is the best project management software for remote teams?", the model does not provide a list of ten blue links. It provides a synthesized answer, often mentioning specific brands, citing URLs, and summarizing features. If your brand is not mentioned in that answer, your Share of Voice for that specific prompt is zero, regardless of how well your blog post ranks on Google.

This shift requires a new class of analytics tools. We need tools that can run automated queries on LLM chatbots like ChatGPT, Anthropic Claude, Google Gemini, and Perplexity. These tools must record the number of times and the specific contexts in which a brand or its content is referred to. They must track "AI answer presence" and "AI brand mention monitoring" to generate actionable intelligence.

Core Capabilities of AI Visibility Tools

To effectively measure SoV in chatbots, one must understand the specific technical capabilities required. The best AI search visibility tools share several core functionalities that differentiate them from traditional rank trackers.

LLM Answer Presence Tracking

The primary function of these tools is to simulate a user querying an AI engine. They run predefined prompts at regular intervals and scrape or analyze the resulting output. This allows marketers to see if their brand is organically suggested by the AI when a potential customer asks for recommendations. This is distinct from paid search; it is about the AI's "earned" recommendation based on its training data and real-time retrieval capabilities.

AI Brand Mention Monitoring

Beyond simple presence, these tools track the sentiment and context of mentions. They analyze the generative answer outputs to find brand mentions and notify users of their occurrences. This includes the location of the mention (beginning, middle, or end of the answer) and the sentiment (positive, neutral, or negative). Some advanced tools offer sentiment analysis features, categorizing mentions as positive or negative, though the depth of this analysis varies by platform.

AI Search Ranking and URL Detection

These tools rank your pages and prompts, locating which specific URLs the AI is referencing, how frequently they are cited, and the position of those citations. This creates an "AI Ranking" metric. If ChatGPT frequently cites a specific whitepaper on your site, you know that content is authoritative in the eyes of the LLM. This helps content teams understand what assets are actually driving visibility within the AI ecosystem.

Content Optimization and Gap Analysis

The most advanced tools go beyond reporting and offer content optimization suggestions. They compare your current data with the demands of AI search to identify inconsistencies. For example, if competitors are being cited for "remote team management" but you are not, the tool might highlight this gap, prompting you to create or optimize content to address that specific prompt cluster.

Analyzing the Top Tools for AI Share of Voice

The market for AI visibility tools is fragmented, with different platforms catering to different organizational needs, budgets, and technical capabilities. Below is an analysis of the leading tools currently available for measuring Share of Voice in AI answers.

OtterlyAI

OtterlyAI positions itself as a bridge between classic rank tracking and the new world of AI assistants. It tracks how often your brand and competitors show up inside AI answers across engines like ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, and Copilot.

• Key Features: It turns mentions and citations into clean AI share-of-voice dashboards and trends. It reveals which prompts you win, which competitors dominate, and what content earns citations.
• Target Audience: It is best for teams treating AI search as a real acquisition channel. This includes in-house SEO and content teams, brand and PR owners, agencies selling AI visibility services, and growth marketers wanting AI visibility KPIs.
• Why Teams Use It: Teams use OtterlyAI to see where they win or lose visibility in AI answers and which specific URLs get cited. It makes AI visibility measurable, driving Generative Engine Optimization (GEO) work with data rather than screenshots and guesswork.

HubSpot AI Share of Voice Tool

HubSpot offers a free AI visibility grader designed to lower the barrier to entry for AI SoV measurement.

• Key Features: The tool checks how your brand shows up across AI engines like ChatGPT, Claude, and Perplexity. It outputs an AI SoV score, competitor context, and optimization suggestions.
• Target Audience: It is tailored for marketing and growth teams already using HubSpot, SMBs and mid-market brands testing AI visibility, and agencies needing a free diagnostic to start AI SoV conversations.
• Why Teams Use It: It serves as a fast baseline to answer the question, "How visible are we in AI today?" It is excellent for initial benchmarking, executive presentations, and before/after checks around specific campaigns or content pushes.

Profound

Profound is an enterprise-grade solution designed for complex tracking requirements.

• Key Features: It offers multi-engine AI answer tracking, topic-level and region-level SoV analysis, and strong exports suitable for Business Intelligence (BI) integration.
• Target Audience: Enterprise organizations and agencies managing large, complex brands with significant digital footprints.
• Why Teams Use It: It provides the depth and scalability required by large brands. The ability to track at a region level is particularly valuable for global companies needing localized visibility data.

Ahrefs

While primarily a traditional SEO suite, Ahrefs has adapted to include SoV metrics, though its focus remains on classic SERPs.

• Key Features: It provides Share of Voice for classic SERPs, deep keyword and backlink data, and identification of competitive content gaps.
• Target Audience: SEO teams wanting a primary SEO/SoV hub that covers traditional search while offering a baseline for brand visibility.
• Why Teams Use It: It allows teams to consolidate their SEO and SoV tracking into one platform, leveraging its massive database of keywords and links to understand the broader competitive landscape.

Semrush

Semrush is a comprehensive suite that integrates SEO, PPC, and content marketing, now including AI visibility modules.

• Key Features: It combines SEO, PPC, and content tools in one suite. It offers AI visibility modules and competitive multi-channel reports.
• Target Audience: Brands wanting to manage SEO and AI SoV within a single toolset.
• Why Teams Use It: It offers a holistic view of digital marketing performance. For teams already using Semrush for other channels, adding AI visibility tracking is a natural extension of their workflow.

Mangools

Mangools focuses on simplicity and budget-friendliness, offering entry-level tools for AI-aware SEO.

• Key Features: It offers simple rank tracking, Share of Search for branded terms, and basic AI search checks. Its Share of Search and AI Search tools act as simple SoV proxies for brands that cannot justify heavy software stacks.
• Target Audience: Freelancers, small teams, and those on a budget.
• Why Teams Use It: It is an easy entry point for AI-aware SEO. The availability of free tools supports early experimentation without financial commitment.

Brandwatch and Brand24

These tools traditionally focused on social listening and PR SoV but have expanded to include web and AI insights.

• Brandwatch: Offers social and PR SoV tracking, advanced sentiment and filters, and enterprise-ready dashboards. It is best for large brands and PR/comms teams.
• Brand24: Provides real-time mentions and SoV, sentiment and reach metrics, alerts, and basic AI insights. It is best for SMBs and mid-market brands.
• Why Teams Use Them: They connect traditional social sentiment with emerging AI mention trends, allowing PR teams to see if their AI visibility correlates with public sentiment.

Mentionlytics

Mentionlytics bridges the gap between social/web listening and AI recommendations.

• Key Features: It offers web and social SoV reports, an AI assistant for insights, and influencer and channel breakdowns.
• Target Audience: SMB to mid-market companies and agencies needing social SoV combined with actionable recommendations.
• Why Teams Use It: It provides a mix of data reporting and AI-driven suggestions, helping teams move from raw data to strategic action.

Comparative Analysis: Features and Pricing

To understand which tool fits a specific organization, it is helpful to compare their core attributes side-by-side. The following tables break down the capabilities and pricing structures of the most prominent tools mentioned.

Table 1: Feature Comparison by Tool Category

Table 2: Pricing and Accessibility Comparison

Deep Dive: Peec AI Visibility Tool

Source [2] provides a detailed look at a specific tool named Peec, which serves as a prime example of the capabilities required for AI Share of Voice tracking. This tool is specifically designed to track visibility at leading AI answer platforms, including ChatGPT, Google AI Overviews (AIO), Perplexity, Claude, Gemini, and Copilot.

Core Capabilities of Peec

Peec functions primarily as a data center. It runs queries on the specified LLMs and chatbots instead of the user, keeping a record of how many times and in what contexts the user's brand or content is referred to. It is not a diagnostic or playbook tool; it provides the raw signals (such as "Competitor X is ahead on these prompts") but leaves the solution implementation to the user.

Sentiment Analysis

A significant feature of Peec is its sentiment analysis. It incorporates a brand sentiment feature that analyzes positive versus negative mentions. However, it is important to note that this feature is only available in the middle-tier plan (Pro), which costs €199 per month. The sentiment indicator is presented as a figure; the tool does not provide sample responses or suggestions for improvement based on that sentiment.

Target Audience and Use Cases

Peec is best suited for small to mid-sized marketing teams, particularly those in the B2B and SaaS sectors who value clean dashboards. It is also advantageous for brands that already benefit from strong SEO traffic and wish to extend that visibility into AI channels. Groups that prioritize sentiment and share-of-voice tools will find value here.

Pricing Structure

Peec offers a transparent, tiered pricing model: * Starter Plan: €89 per month. Includes 25 prompts, tracking in 3 countries, tracking on ChatGPT/Perplexity/AIO, and unlimited seats. This is highly advantageous for agencies or distributed teams. * Pro Plan: €199 per month. Includes 100 prompts, tracking in 5 countries, and sentiment analysis. * Enterprise Plan: €499 per month. Includes 300+ prompts.

Limitations

Despite its strengths, Peec has distinct limitations. It does not provide estimates of how much website traffic the AI-generated mentions are driving. Furthermore, because it acts as a data center rather than a diagnostic tool, users must manually interpret the data to formulate strategies.

Building an Effective AI SoV Measurement Stack

Selecting a single tool is rarely sufficient for a comprehensive strategy. Organizations should build a measurement stack that combines different types of data to form a complete picture of their AI visibility.

Step 1: Establish a Baseline with Free Tools

Start with free tools like the HubSpot AI Share of Voice Tool. This provides a quick snapshot of where the brand stands today without financial investment. It is ideal for executive reporting and initial benchmarking.

Step 2: Implement Dedicated Tracking

For ongoing monitoring, invest in a dedicated AI visibility tool like OtterlyAI or Peec. These tools provide the granular data needed to track trends over time. When evaluating these tools, look for: * Coverage of all major LLMs (ChatGPT, Gemini, Claude, Perplexity). * Tracking of AI Overviews (Google's generative search results). * The ability to track specific prompts relevant to your business.

Step 3: Integrate with Traditional SEO

Do not abandon traditional metrics. Use tools like Ahrefs or Semrush to maintain visibility on keyword rankings and backlinks. The relationship between traditional SEO and AI visibility is symbiotic. Strong traditional rankings often feed the training data of LLMs, leading to better AI visibility.

Step 4: Analyze Sentiment and Context

If sentiment analysis is a priority, consider tools like Brandwatch, Brand24, or the Peec Pro plan. Understanding not just if you are mentioned, but how you are mentioned, is crucial for reputation management. A high SoV with negative sentiment is a risk, not a win.

Step 5: Close the Loop with Content Optimization

The ultimate goal of measuring AI SoV is to improve it. Use the data from your tracking tools to inform your content strategy. If you notice competitors are being cited for specific prompts that you are missing, create high-quality content addressing those prompts. This is the practice of Generative Engine Optimization (GEO).

Key Terminology for AI Visibility

To navigate this new landscape, it is essential to understand the specific terminology used within the industry.

• Share of Voice (SoV): In the AI context, this is the percentage of mentions a brand receives compared to competitors within AI-generated answers for a specific set of prompts.
• Generative Engine Optimization (GEO): The practice of optimizing content to improve visibility and citations within AI-generated answers, distinct from traditional SEO which focuses on ranking in SERPs.
• AI Overviews: Google's feature that generates a summary of answers at the top of the search results page, often pulling from multiple sources.
• LLM (Large Language Model): The underlying AI technology (e.g., GPT-4, Claude 3) that powers chatbots and generative search features.
• Prompt Tracking: The act of monitoring specific user queries to see which brands and URLs are referenced in the AI response.
• Sentiment Analysis: The computational process of determining whether a mention of a brand is positive, negative, or neutral.

Frequently Asked Questions

How is AI Share of Voice different from traditional Share of Voice? Traditional SoV measures visibility in SERPs (search engine results pages) or media mentions. AI SoV measures visibility inside the text generated by AI chatbots and search overviews. It is about being part of the answer, not just a link on the page.

Do I need to pay for these tools? While free tools like HubSpot's offering exist, comprehensive tracking requires paid solutions. The cost varies significantly, from budget-friendly options like Mangools to enterprise-level investments like Profound or Brandwatch.

Can these tools estimate traffic from AI mentions? Most tools in this category focus on visibility and mentions rather than traffic attribution. Source [2] notes that Peec, for example, does not provide estimates of how much website traffic AI-generated mentions drive. This remains a complex challenge in the industry.

Is sentiment analysis accurate? Sentiment analysis is generally reliable for broad trends but can miss nuance. It is best used as a directional metric rather than an absolute truth. Most tools provide a sentiment score rather than detailed analysis of why a sentiment is negative or positive.

The Bottom Line: Owning the Conversation

The shift to AI-driven discovery is not a future trend; it is the current reality of how customers find information and make decisions. Brands that continue to rely solely on traditional SEO metrics will find themselves invisible in the channels that matter most. Measuring Share of Voice in chatbots and AI answers is the first step toward reclaiming that visibility.

By leveraging the tools outlined in this guide—from free baselines like HubSpot to dedicated trackers like OtterlyAI and comprehensive suites like Semrush—organizations can gain the data necessary to compete. The goal is not just to be mentioned, but to be recommended. It is to ensure that when an AI assistant answers a user's question, your brand is the one being cited as the authority, the solution, and the trusted choice. The era of the blue link is fading; the era of the AI answer has arrived.

Sources

1. Best AI Tools for Monitoring Share of Voice in AI Answers
2. Best AI Visibility Tools