This week’s Tech Tuesday covers tools that help you see where your brand appears in AI-generated answers, which competitors show up instead, and which sources are shaping those results.
The shift away from traditional search is accelerating faster than many business owners realise. When someone asks ChatGPT “What’s the best accounting software for small businesses?” or queries Perplexity about “top email marketing tools for e-commerce,” the AI generates an answer on the spot, often without sending the user to any website at all.
For decades, businesses optimised their sites to rank on Google’s first page. Now, a new layer sits between the customer and your website, and many companies have no idea whether they’re visible in it. AI search looks beyond your site. It sees your brand everywhere it appears, in reviews, Reddit threads, YouTube videos, press coverage, and product listings. This scattered presence across the web determines whether large language models consider your business worth mentioning when answering customer queries.
This week’s Tech Tuesday highlights a few tools that can help you stay visible in this new landscape.
Ahrefs Brand Radar
Ahrefs Brand Radar tracks how a brand, product, or entity appears inside AI search and LLM-driven answer engines such as ChatGPT, Perplexity, Gemini, and Microsoft Copilot. It stands out by combining AI-index visibility with traditional SEO demand signals, giving teams a unified view of where their brand is mentioned, how often, and in what context across both AI and web ecosystems.
The platform ingests AI-mention and citation counts from six major AI indexes and maps them alongside branded search-volume trends going back to 2015 and web-mention histories dating to 2013. It supports competitive benchmarking, highlighting where rivals appear in AI answers and where your brand is absent, and surfaces the URLs and domains most frequently triggering AI mentions. The main trade-off is granularity: AI-visibility data updates monthly rather than in real time, and personalisation in LLM responses isn’t fully captured.
Best suited for mid-market and enterprise teams already using Ahrefs for SEO who now need visibility into how LLMs reference their brand. Not ideal for very small brands or early-stage startups with minimal web presence, since insights depend heavily on existing content signals.
Semrush AI SEO Toolkit
Semrush’s AI SEO suite targets brand and content visibility within AI-driven search engines and LLM-based answer systems, tracking where your domain appears in AI Overviews, LLM responses and prompt-based discovery. Unlike legacy SEO tools that only monitor rankings, this toolkit surfaces how your brand is cited in generative AI results and where gaps exist versus competitors.
The Visibility Overview dashboard shows your brand’s share of voice in ChatGPT, Google AI Overviews, Perplexity and other platforms. Prompt-level tracking reveals exact queries where you’re cited or missing, while a Cited Pages view links to URLs used by LLMs. Integration with Semrush’s broader keyword, backlink and content modules allows teams to align AI visibility with traditional SEO performance. The trade-off: data is still accumulating, regional depth varies and many features require their Enterprise-class plan.
Best for mid-to-large enterprises or agencies that already use Semrush for SEO and wish to extend visibility into AI/LLM channels, especially if they have the content and technical backup to act on prompt-level insight.
Amplitude AI Visibility
Amplitude AI Visibility is Amplitude’s module for tracking how a brand appears inside AI-search and LLM-generated answers, including ChatGPT and Google AI Overview. It differentiates itself by tying LLM visibility directly to downstream behavioural metrics inside Amplitude, something traditional SEO platforms can’t do. The main limitation is coverage: the product currently monitors only a subset of AI surfaces, and historical benchmarking depth is still limited because the product is relatively new.
The platform provides a weekly Visibility Score that quantifies how often a brand is mentioned, how it ranks relative to competitors, and what sources LLMs cite. Users can inspect actual prompts where their brand appears or is absent, review which URLs were referenced by the model, and compare visibility by model type. Because it is built into Amplitude, teams can connect AI-search visibility to product usage, cohorts, funnels, and revenue, enabling attribution and experimentation workflows.
Best for mid-size to enterprise teams already using Amplitude for product analytics and wanting AI-search visibility tied to real customer behaviour. Not ideal for organisations seeking stand-alone SEO tooling or deeper keyword-level optimisation outside the Amplitude ecosystem.
Scrunch AI
Scrunch AI is a specialist platform for tracking how brands and their content perform within AI-search and large-language-model answer engines, helping marketers see when their site is cited in responses by systems like ChatGPT, Gemini, Perplexity and Google AI Overview. Its differentiator is the AXP (Agent Experience Platform) which claims to generate a parallel “AI-friendly” version of your website to maximise citations, though many optimisation features remain labelled as beta.
Scrunch offers prompt-level monitoring across multiple AI engines, letting you filter by model, region, persona and topic to see where your brand appears or doesn’t. It provides citations mapping (which pages are referenced by AI) and AI bot crawl tracking via GA4 integration to link content performance with AI visibility. It supports enterprise security (SOC 2 Type II, RBAC) and multi-brand, multi-region workflows. The trade-off: specific prompt-volume transparency is limited and some reviews point to weekly data refreshes rather than true real-time updates.
Best for mid-to-large organisations or digital agencies managing brands that need to measure and improve how they are cited in AI-generated answers, particularly if they already operate multi-sites or multi-regions and want enterprise-grade monitoring.
ZipTie.dev
ZipTie.dev is a specialised tool for monitoring brand visibility across AI-search and LLM-driven answer engines, particularly tracking presence in Google AI Overviews, ChatGPT and Perplexity. The platform differentiates itself by offering query-level insight into whether your domain is cited in AI Overviews and which queries trigger them, a capability few traditional SEO tools provide. One trade-off: the tracking is currently focused on a limited set of AI-search engines, and the refresh frequency may be weekly rather than realtime.
ZipTie supports monitoring of AI-search visibility by letting you upload queries or import from Google Search Console and then checking whether AI Overviews exist for those queries, whether your domain is cited as a source, and your share versus competitors. The platform also offers content-optimisation suggestions tailored for AI-search (so you can adjust pages to improve your chance of citations), and supports multi-region tracking (US, UK, Australia, Brazil, India). Deployment is cloud-based, setup appears quick, but some users report slower processing times and a credit-based model for query checks.
Best for mid-sized to larger brands or SEO/marketing teams with existing content and web presence that want to measure and optimise inclusion in AI-search results, especially if they already monitor Google Search Console and want next-gen Answer Engine Optimisation.
Gumshoe AI
Gumshoe AI is a visibility platform that scans major AI search models, such as ChatGPT, Google Gemini and Perplexity, to measure how often your brand appears in AI-generated responses, which sources are cited and which personas and topics drive mentions. Its standout is persona plus model-specific visibility reporting (your brand’s share of voice for specific audience segments on ChatGPT) and Cited Sources tracking. A limitation is that some features still rely on periodic snapshots (not real-time) and pricing is based on conversations, which may scale unpredictably.
Gumshoe enables you to define focus areas (brand, product), personas and topic sets, then run multi-model reports that surface visibility scores, competitor leaderboards, citation domains and prompts where your brand appears or is absent. You can export reports, set up scheduled runs, and access optimisation suggestions (which pages should earn more citations, which sources to influence). Deployment is cloud-based with pay-as-you-run pricing (first runs free then around ten cents per conversation).
Best for mid-to-large organisations, brands or digital teams with established web content who want to track and benchmark how they appear in LLM-driven search and answer engines. Particularly suited for marketing and SEO teams tasked with AI citations and share-of-voice across AI models.
LLMrefs
LLMrefs tracks brand and content visibility specifically within AI-search and large-language-model responses (ChatGPT, Google Gemini, Perplexity). It differs from traditional SEO tools by focusing on whether your domain or content is cited in AI-generated answers rather than simply ranking in a search engine. A trade-off: the data-refresh cadence is typically weekly and the platform assumes you have sufficient content and web footprint to generate meaningful citations.
LLMrefs offers keyword-tracking across multiple AI models, not just Google or traditional SERPs. It calculates a proprietary LLMrefs Score to quantify your visibility as a cited source inside AI responses. It reports which URLs are being referenced by models and monitors competitor visibility across the same queries. Limitations: the coverage of smaller or niche LLMs may be incomplete, and the tool appears oriented towards keywords rather than full prompt sets, which may restrict insight depth for highly conversational or generative queries.
Best for mid-sized to large brands and agencies managing content-rich web portfolios who want to augment SEO with Answer Engine Optimisation. Not ideal for very small businesses or brands with minimal web presence or who lack the content baseline to be cited by AI models.
Mangools AI Search Watcher
Mangools AI Search Watcher monitors how a brand appears in AI-search engines (ChatGPT, Google Gemini, Claude, Mistral, Llama) to track brand citation, trust and visibility inside AI-generated answers rather than traditional SERP ranking. What sets it apart is its prompt-based tracking across major LLMs, showing where you’re cited (or not) versus competitors. A limitation: it currently focuses on visibility (whether you’re cited by AI responses) rather than comprehensive content gap analytics or full-stack AI-search funnel performance.
The tool lets you define prompts (or use its suggested prompt library), and runs each prompt multiple times (five times) per model to derive average visibility and citation counts. It supports side-by-side brand versus competitor visibility reports across models and displays which domains and URLs are being cited by the models. Implementation is simple (set up a monitor in roughly 30 seconds) but the model set is fixed (six major models) and data refresh cadence and depth for long-tail queries may lag.
Best for marketing or SEO teams in organisations with an existing content and web footprint who want to extend into generative AI and LLM visibility. Ideal for medium-sized firms or agencies delivering reports on AI visibility.
AIclicks
AIclicks is a visibility platform built for the era of AI-search and large language model answer engines. It tracks whether a brand appears in responses from models like ChatGPT, Gemini or Perplexity and goes beyond traditional SEO by focusing on citations and mentions rather than keyword rank alone. Its standout is prompt-level tracking and daily refreshes, although it requires sufficient query volume and content base to generate meaningful insights.
It supports prompt definition (you upload or select prompts) and monitors how your domain or content is cited across multiple AI models (including ChatGPT, Gemini, Perplexity, and in higher tiers Google AI Overviews). You get competitor benchmarking, identifier of which sources and domains get cited in AI responses, and built-in content action suggestions (missing prompts, content gaps) for Generative Engine Optimisation. The platform is cloud-based, offers daily data refreshes and country-based monitoring. A limitation noted: the system lacks deep enterprise-grade historical datasets and still depends on your web and AI presence to draw actionable insight.
Best for mid-to-large brands and agencies with an established content footprint who want to measure and improve how they appear in AI-search and LLM channels. Not ideal for very small organisations or brands with minimal online content, because the value depends on tracking sufficient prompts and citations.
Goodie
Goodie is an AI-search visibility platform built to help brands monitor how large language models (ChatGPT, Gemini, Claude, Perplexity) describe, rank, and recommend them. Its differentiator is scope: instead of focusing on traditional SERPs, Goodie tracks brand mentions, competitor references, and answer composition directly inside AI-generated responses. This makes it useful for teams treating LLMs as emerging answer engines, but its coverage varies by model and geography, which limits granularity in some markets.
Goodie continuously queries major LLMs and extracts structured signals such as brand frequency, sentiment, product recommendations, category share-of-voice, and competitor substitutions. It provides dashboards showing who LLMs recommend for category queries (“best X tools,” “top Y brands”), how often a brand appears, and whether it is being misrepresented. The platform supports multi-model comparison, alerting, and historical trend analysis. Current limitations include lack of full API-level transparency from some LLM providers, meaning update intervals and sample breadth may fluctuate depending on the model.
Best for marketing and growth teams that need to audit how LLMs summarise their products, track competitive positioning, and correct misinformation. Ideal for consumer brands, DTC, SaaS, and marketplaces investing in AI answer optimisation.
Profound
Profound is a platform designed to monitor and optimise how brands appear in AI-powered search and answer engines, often called Answer Engine Optimisation. It distinguishes itself by tracking visibility across major models (ChatGPT, Perplexity, Google AI Overviews, Microsoft Copilot, Gemini) and correlating citations, prompt volumes and traffic back to brand content. The trade-off: it’s positioned for enterprise budgets and assumes a significant web and content footprint.
The platform offers three core modules: Answer Engine Insights (visibility score, share of voice, domain citation analytics), Prompt Volumes (estimates of how many users ask specific questions on AI-search models, weekly updates) and Agent Analytics (tracks how AI-crawlers access your site, attribution of traffic from AI-search). It supports enterprise-grade security (SOC 2-Type II, SAML/SSO) and is cloud-based, but the base tier limits API access and historical data retention, meaning teams with smaller scale may find value constrained.
Best for large brands, marketing operations teams and agencies with existing content and deployment resources that wish to quantify and improve how their content appears in the AI-search and LLM layer across models and channels. Not ideal for smaller organisations or sites without sufficient content or visibility.
Peec AI
Peec AI is a brand visibility platform tailored for the age of AI-search and LLM-driven discovery: it tracks how brands appear (and are cited) within AI answer engines such as ChatGPT, Gemini, Perplexity and Claude. What sets it apart from classic SEO tools is its focus on citations, prompts, visibility plus sentiment scores rather than just ranking for keywords in Google. The trade-off is that effective use still assumes a solid content and visibility baseline and may require marketing and SEO resource investment.
Peec AI supports prompt-library creation and tracking across AI models, offering visibility, position and sentiment metrics for each brand, query, model pair. It also reveals which domains and pages are cited by the AI engine for specific prompts, allows competitor benchmarking and integrates with external dashboards (via API or Looker Studio) for reporting. A current limitation: coverage is optimised for large brands and marketing teams, data refresh or historical depth may be limited for smaller sites, and the system may require time to populate meaningful prompt-level insights.
Best for medium to large organisations or agencies with existing content assets and digital presence who want to evaluate and improve how they are discovered in AI and LLM-driven search rather than just traditional SERPs. Suitable when you track traffic from LLMs or want to invest in Answer Engine Optimisation.
AirOps Insights
AirOps Insights is a visibility platform focused on how brands appear in AI-powered search and answer engines (LLMs) and aligns that with traditional SEO and content performance. It stands out by combining visibility metrics (mentions, citations) across AI platforms with content-and-workflow orchestration modules, a dual capability many tools separate. The trade-off is that it’s built for content-engineering teams with scale and may be more than what smaller or less mature brands require.
The product offers a unified view of AI visibility, traditional search analytics and site performance to diagnose gaps and action points. It allows users to track brand citations in LLM responses, prioritise content refreshes, and execute workflows through built-in collaboration modules. Implementation is cloud-based, integrates with CMS and analytics platforms, but the dataset refresh cadence and depth (prompt-level across all major LLMs) are less transparent than some niche tools.
Best for mid-to-large organisations with active content operations teams, an existing web presence and a desire to target AI-search and LLM visibility rather than just traditional SEO. Well suited when you’re publishing at scale and need workflow orchestration, not just monitoring.
ProductRank
ProductRank is a lightweight visibility tool that checks how different AI models, such as ChatGPT, Claude and Perplexity, rank products in a given category and whether your brand’s products are being cited among top results. It stands out for its simplicity and no-cost entry (at least for basic usage) compared to enterprise-grade AI visibility platforms. The trade-off is that it lacks depth of analytics and monitoring cadence typical of more mature tools, making it less suitable for full-scale brand visibility programmes.
It allows users to input a product category (CRM software, running shoes) and retrieves how each participating AI model ranks the top products, displays which domains or sources the models cite in those rankings, and gives a quick snapshot of visibility gaps relative to competitors. Data refresh and model coverage appear limited and the tool is primarily designed for ad-hoc checks rather than continuous monitoring.
Best for small to mid-sized e-commerce brands or product teams wanting to quickly scan how their products stack up in AI-search recommendation contexts, especially when cost is a constraint. Not ideal for large enterprises or agencies seeking ongoing prompt-level monitoring, dashboards, or full competitive benchmarking across many categories, because analytics scope and refresh cadence are limited.
Hall AI
Hall AI is a dedicated visibility platform for tracking brand presence inside AI-powered search and conversational agents (ChatGPT, Google Gemini, Claude, Microsoft Copilot). What differentiates it is an emphasis on citations and agent analytics. Users can see when their website is referenced by AI systems or visited by AI crawlers, rather than just traditional keyword ranking. A trade-off: the data cadence is often weekly or daily depending on plan, and some advanced integrations (full GA4 attribution) appear limited.
The platform allows monitoring of Generative answer insights (share-of-voice, sentiment across prompts) and Website citation insights (which pages are referenced by AI conversations) as well as Agent analytics tracking how bots access your site. Coverage includes major models and answer engines (ChatGPT, Gemini, Copilot, Perplexity). Implementation appears fast for base monitoring (you upload or select prompts and topics), though higher-volume or enterprise setups include API access and custom data export. One limitation: for smaller brands with limited content and visibility the data may be sparse and value reduced.
Best for mid-to-large brands, content and SEO teams or agencies that already publish at scale and want to extend monitoring into the Answer Engine Optimisation channel, tracking how AI-agents mention and route traffic to your site.
Nimt AI
Nimt.ai is a SaaS platform built to track how brands and content are cited within AI-search and large-language-model environments, such as ChatGPT, Claude, Gemini and Perplexity. It differentiates itself by offering multi-language, multi-model visibility, showing which prompts trigger mentions, competitor comparisons, and source-domain breakdowns. A limitation: the company is early stage (seed funded in 2025) and some functionality (full historic prompt depth or broad LLM coverage) appears still maturing.
Nimt.ai allows users to input brands or URLs and tracks brand share of voice in AI answer engines, shows how many times a brand is cited by a model for selected prompts, compares brand versus competitor visibility, and identifies which domains are influencing citations. The platform supports daily tracking across multiple models, multi-brand and multi-location deployments, and multilingual prompts. A trade-off: coverage for niche or less visible brands may be thin, and the dataset refresh rate and prompt-sample size may be limited for smaller organisations.
Best for marketing and SEO teams in medium to large organisations or agencies that already manage a substantial content and web presence and want to understand and optimise how they appear in AI-search and LLM contexts. Less well suited for very small businesses or brands with minimal web content.
AthenaHQ
AthenaHQ is a platform focused on optimising brand visibility across AI-driven search and large language model environments, a category often called Generative Engine Optimisation. What sets it apart is its emphasis on measuring how brands and content are cited in generative answer engines (ChatGPT, Google Gemini, Claude) rather than just their traditional keyword ranking in search engines. A key trade-off: despite its rich capabilities, AthenaHQ is positioned at the enterprise level and may involve higher cost and setup efforts compared to traditional SEO tools.
AthenaHQ offers a suite of tools including its proprietary Query Volume Estimation Model (QVEM) that estimates conversational prompt volumes across multiple AI platforms and forecast trends. The platform tracks brand mentions and citations in AI answer-engines, provides competitive benchmarking across models, and offers actionable recommendations to improve GEO metrics (configuring llms.txt, entity-rich content structuring). Deployment is cloud-based and supports multi-model coverage, but smaller organisations may find the data-entry and technical adaptation requirements heavier than simpler SEO tools.
Best for mid to large companies, particularly those with substantial content assets and existing SEO or digital-marketing teams, looking to extend into AI-search and LLM visibility and capture attention in generative answer engines. Not ideal for very small businesses or teams without sufficient content volume, resources for implementation, or a need to monitor across multiple AI models.
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