Chatbot Audit Tools: See If LLMs Recommend Your Brand

Chatbot Audit Tools: Best Ways to See if LLMs Recommend Your Brand

I still remember the first time I ran a serious brand audit on ChatGPT. I wasn’t looking for code or creative writing; I simply asked, “What is the best payroll software for a 20-person startup in the US?”

My client—a leading player in that exact niche—was nowhere to be found. Instead, the model confidently recommended three of their biggest competitors and a company that had gone out of business two years prior. That was the moment I realized that chatbot audit tools aren’t just a nice-to-have for SEOs; they are essential visibility reports for modern businesses.

If you are reading this, you probably suspect your brand is being misrepresented or ignored by the AI assistants your customers use daily. In this guide, I’ll walk you through a practical, blended approach: using manual prompts for deep insights and automated tools for scale. I’ll share the exact scorecard I use, a comparison of the best tools on the market, and a remediation plan to fix what you find.

Why AI recommendations matter now (what’s changing for US buyers)

We are witnessing a fundamental shift in how people find answers. It’s no longer just about ten blue links; it’s about synthesized answers.

The business case for auditing your presence in Large Language Models (LLMs) is simple: AI assistants are the new shortlist. A 2025 study by Commerce and Future Commerce reports that approximately one-third of Gen Z and one-quarter of Millennials now rely on AI platforms rather than traditional search channels for shopping advice .

When a potential customer asks Gemini or Perplexity for a recommendation, they aren’t browsing; they are deciding. If your brand is invisible, you aren’t just missing traffic—you’re missing the consideration set entirely.

I used to think SEO ended at Google. Now, I see “AI Engine Optimization” (AEO) as a critical layer of reputation management. The risks here are specific:

• Invisibility: The model simply doesn’t know you exist.
• Mispositioning: You are a premium enterprise solution, but the AI describes you as a “cheap tool for freelancers.”
• Factual Risk: The model quotes your pricing from 2021 or claims you are non-compliant with HIPAA.
• Sentiment Drift: Over time, the tone of mentions shifts from “innovative” to “buggy” without you realizing it.

An audit helps you establish a baseline so you can stop guessing and start fixing.

What are chatbot audit tools (and what should they measure)?

At its core, a chatbot audit tool is any platform or framework used to evaluate how an LLM represents your brand. This can range from a simple spreadsheet where you log manual checks to sophisticated SaaS platforms that monitor share of voice in real-time.

Regardless of the method, I always measure the same dimensions. If you don’t have a structured way to score these, you are just reading text, not auditing data.

Beginner Audit Scorecard

I use a simple 0-2 scale for most of these metrics to keep things objective.

Metric	What to look for	Scoring (0-2)	Evidence to Save
Presence	Is the brand mentioned at all in the answer?	0 = No 1 = Brief mention 2 = Detailed profile	Screenshot of full answer
Positioning	Is the category and use-case accurate?	0 = Wrong category 1 = Vague 2 = Accurate niche	Text snippet of description
Sentiment	Is the tone positive, neutral, or negative?	0 = Negative 1 = Neutral 2 = Positive	Adjectives used (e.g., “reliable,” “expensive”)
Accuracy	Are specific facts (price, features) correct?	0 = Hallucination/Error 1 = Outdated 2 = Correct	Link to source vs. claim
Citations	Does the model link to valid sources?	0 = No links 1 = Competitor/Random links 2 = Brand/Authority links	URLs of citations

Quick answer: manual vs automated audits—what I use and when

People often ask me if they should buy a tool immediately. The reality is, start manual. You need to understand how the models “think” about your specific brand before you automate the tracking.

Use Manual Audits when: You are establishing a baseline, testing new messaging, or digging into a specific PR crisis.

Use Automated Tools when: You need to track trends over weeks, benchmark against 5+ competitors, or monitor multiple product lines across every model update.

My blended workflow: how I run an audit with chatbot audit tools + a manual prompt bank

Here is the exact workflow I use. You can do this in about 60 to 120 minutes for a single brand. I recommend setting up a “Audit flow” visual in your head: Prompt bank → Run across models → Log evidence → Score → Fix.

Step 1: pick the exact buyer questions I want to win

Don’t just ask “What is Brand X?” That is vanity searching. You need to mimic the buyer’s journey. I break my prompts down into three intent clusters:

• Discovery (Unbranded): “What are the best CRM tools for a real estate agency in Florida?”
• Comparison (High Intent): “Compare Brand A vs Brand B for enterprise security features.”
• Trust/Validation: “Is Brand X legit?” or “What are the common complaints about Brand X?”

My Copy/Paste Prompt Bank Template:
1. [Discovery] “Recommend 5 [Category] tools for [Specific Persona].”
2. [Comparison] “What is the main difference between [My Brand] and [Competitor]?”
3. [Pricing] “How much does [My Brand] cost for a team of 10?”
4. [Weakness] “What are the downsides of using [My Brand]?”
5. [Features] “Does [My Brand] support [Key Feature/Integration]?”

Step 2: run prompts across models (and control what I can)

Consistency is the enemy of AI testing because models are non-deterministic—they might give different answers twice in a row. To control for this, I follow a strict protocol:

• Models to test: ChatGPT (GPT-4o), Claude 3.5 Sonnet, Gemini (Advanced), and Perplexity.
• Settings: I usually run these in a fresh browser window or “Incognito” mode to minimize personalization bias.
• Repetition: I run the most critical prompts 3 times. If the brand is missing in 2 out of 3, that’s a visibility issue.

Note: Always log the date and model version. “ChatGPT” isn’t a version; “GPT-4o, Jan 2026” is.

Step 3: score and summarize the baseline in one page

Once I have my spreadsheets filled, I create a one-page executive summary. Most stakeholders don’t want to read raw logs; they want to know the risk level.

My summary includes:

• Mention Rate: “We appeared in 40% of discovery queries.”
• Share of Voice: “Competitor Y appeared in 80%.”
• Accuracy Alert: “Gemini is quoting our 2022 pricing structure.”
• Top Citations: “Perplexity is citing a negative Reddit thread instead of our documentation.”

Choosing chatbot audit tools: what to look for (and which tools fit which team)

Once you have done a manual baseline, you might realize that doing this weekly is unsustainable. That is when dedicated software becomes necessary. Whether you are using a dedicated AI SEO tool or a specialized monitoring platform, the goal is consistent data.

Here is a comparison of the current market leaders based on my research. I’ve categorized them by who they serve best.

Chatbot Audit Tools Comparison

Tool	Best For	Key Features	Approx. Cost*	Watch-outs
Rankscale	Small Teams / DIY	Visibility tracking, simple dashboard	~$20/mo	Limited enterprise history
Otterly	Solo Founders	Brand mention tracking, sentiment	~$29/mo	Coverage can vary
Topify	Growth Teams	Sentiment trends, Share of Voice dashboards	Custom/Tiered	Validate data manually
Peec AI	Tech-Forward Marketers	Prompt-response mapping, real-time alerts	Tiered	Newer to market
Profound	Enterprise / Legal	SOC 2, SSO, broad platform coverage	Enterprise	Higher learning curve
Brand24 (Chatbeat)	PR & Brand Managers	Integrates with social listening	Varies	Focus is broader than just LLMs

*Prices are estimates based on available data and may change. Always check official pricing pages.

Starter stack (solo/SMB): where manual audits still do most of the work

If you are a solo marketer or small business, you don’t need to spend thousands. I recommend a subscription to Otterly or Rankscale to keep a pulse on basic mentions, but rely on your monthly manual “spot checks” for the deep nuances of product positioning. Automation is great for “are we there?” but humans are better at “do we sound good?”

Growth/enterprise stack: when alerts, governance, and personas matter

For larger organizations, a wrong answer can be a compliance nightmare. If an LLM hallucinates a refund policy that doesn’t exist, you have a problem. In these cases, tools like Profound or Scrunch AI are valuable because they offer persona segmentation (seeing how results differ for a CTO vs a CFO) and security compliance like SOC 2.

Advanced auditing (optional): LLMAuditor and LLM-as-a-Judge for scalable quality checks

If you are managing visibility across fifty different products or multiple regions, manual scoring fails. This is where frameworks like LLMAuditor come in.

These are technically “human-in-the-loop” workflows where you use an LLM to grade another LLM. It sounds meta, but it works. You essentially set up a “Judge” model (usually a high-reasoning model like GPT-4) to evaluate the outputs of other models based on a rubric you define.

A simple “judge prompt” I use to score accuracy and citations

You don’t need to be a coder to try this. Here is a prompt I use to speed up my scoring:

“You are an expert auditor. I will provide you with a Question, a Model Answer, and a list of Verified Facts. Your job is to score the Answer on a scale of 1-5 for Factual Accuracy.

– Question: [Insert Prompt]
– Model Answer: [Insert Output]
– Verified Facts: [Insert Truth]

Output the score and list any specific hallucinations.”

Just be careful—automated judges can hallucinate too. I always audit the auditor periodically.

After the audit: a practical remediation playbook to influence LLM outcomes

So, you found out that ChatGPT thinks you are out of business, or Gemini hates your pricing. Now what? Remediation is the hardest part of AEO because you cannot just “edit” the database. You have to influence the training data and the retrieval sources.

I treat this like a triage operation. I fix the sources I control first.

1. Fix Factual Sources (Owned Media): If the LLM says your price is wrong, check your pricing page. Is it clear? Is it buried in a PDF? Update your “About Us,” “FAQ,” and “Pricing” pages with simple, declarative sentences.
2. Improve “AI-Citable” Signals: LLMs love structure. Use clear headings (H2s/H3s) and definition lists. Using a structured AI article generator can sometimes help structure content in a way that machines parse easily, provided you verify the facts.
3. Build Third-Party Corroboration: If Perplexity cites Reddit or G2, you need to ensure those profiles are accurate. You can’t force an edit, but you can respond to reviews and update your profiles on directory sites.
4. Schema Markup: Implement Organization, Product, and FAQPage schema. This gives search engines (and the AI overviews powered by them) structured data to rely on.

The “citable page” checklist I use before I rerun audits

Before I consider an issue “fixed” and rerun my prompts, I check the source URL against this list:

• Is the H1 clear and relevant to the entity?
• Is there a direct definition paragraph (e.g., “[Brand] is a…”)?
• Are key facts (price, location) visible without clicking/scrolling?
• Is the “Last Updated” date current and truthful?
• Is the Organization Schema valid?
• Are there internal links from the homepage to this page?

Common mistakes, FAQs, and next steps (so I can keep improving month over month)

Auditing AI visibility is a marathon, not a sprint. The models update constantly, and your results will fluctuate. To keep your sanity, avoid these common traps.

Common mistakes (and the fixes I apply immediately)

• Only testing one model: Just because you rank in ChatGPT doesn’t mean you exist in Gemini. Fix: Always test the big three (OpenAI, Google, Anthropic).
• Changing prompts every time: If you change the question, you can’t compare the answer. Fix: Save your prompt bank and stick to it.
• Ignoring “Browsing” mode: Some answers come from training data; others come from live web searches. Fix: Note whether the model browsed the web in your logs.
• Focusing on vanity metrics: Getting mentioned is useless if the sentiment is negative. Fix: Prioritize sentiment and accuracy over raw mention count.

FAQs about auditing LLM brand recommendations

What is a chatbot audit tool?
A tool or workflow that measures how AI models perceive and recommend your brand. It tracks metrics like share of voice, sentiment, and factual accuracy.

Manual vs automated audits—which is better?
Manual is better for deep qualitative analysis and setting baselines. Automated is better for scaling that analysis across time and competitors.

How often should I audit my brand across AI models?
For most businesses, a quarterly deep dive (manual) and monthly automated check is sufficient. If you are in a volatile industry, increase this frequency.

Which LLM platforms should I focus on?
Prioritize ChatGPT (highest market share), Google Gemini/AI Overviews (search integration), and Perplexity (high purchase intent).

What actions follow an audit?
Update your website content, fix schema markup, correct directory listings, and create new content that directly answers the buyer questions where you were missing.

Next steps: my 30-day plan (baseline → fixes → re-test)

If you want to get started today, here is a realistic plan:

• Day 1: Create your prompt bank (Discovery, Comparison, Trust).
• Day 2-3: Run the manual baseline audit across ChatGPT, Gemini, and Perplexity.
• Day 7: Build your scorecard and identify the top 3 “Red Flags” (e.g., wrong pricing).
• Day 14: Ship fixes to your website (Schema, FAQs, updated stats).
• Day 30: Rerun the audit prompts and check for improvement.

The goal isn’t perfection; it’s visibility. By treating these chatbots as a marketing channel rather than a black box, you turn a potential risk into a competitive advantage.