How to Fix Wrong Recommendations About Your Brand in Perplexity

In the rapidly evolving landscape of search, Perplexity AI has emerged as a powerhouse, blending traditional search indexing with large language model (LLM) reasoning. However, as more consumers turn to "answer engines" to make purchasing decisions, a new challenge has surfaced: AI misinformation. If you have noticed inaccuracies or outdated information surfacing in searches, you must learn how to fix wrong recommendations about your brand in Perplexity to protect your market share and digital reputation.

When Perplexity provides a "wrong" recommendation—such as suggesting a competitor for a niche you dominate or misrepresenting your product features—it isn't just a technical glitch. It is a fundamental failure of brand accuracy in the AI ecosystem. Unlike traditional SEO, where you fight for a blue link, Perplexity SEO is about influencing the synthesized narrative that the AI presents to the user.

1. Diagnosing the Issue: Why Wrong Recommendations Matter

The impact of a halluncinated or incorrect recommendation in Perplexity is far more damaging than a low ranking on Google. In traditional search, a user sees a list of options. In Perplexity, the AI often provides a definitive, synthesized answer that sounds authoritative.

The Impact on the Marketing Funnel

• Loss of Trust: If Perplexity claims your software lacks a feature it actually possesses, potential customers will drop out of the funnel before even hitting your landing page.
• Competitor Advantage: Perplexity often lists "Top 3" or "Best of" recommendations. If your brand is omitted or misrepresented while a competitor is praised, you are losing "share of model."
• Reputation Management Crisis: AI misinformation can scale quickly. Because LLMs train on available data, one incorrect blog post or an outdated Wikipedia entry can poison the well for thousands of AI-generated queries.

Identifying the "Wrong" Recommendation

Diagnosis starts with understanding the type of error. Is the AI citing an outdated price? Is it confusing your brand with a similarly named company? Or is it simply failing to "see" your brand as a relevant solution for a specific category? Identifying the root of the error is the first step toward perplexity visibility optimization.

2. Root Causes: Why Perplexity Gets Your Brand Wrong

To fix the problem, you must understand how Perplexity works. It is a "Proactive Search" engine that uses RAG (Retrieval-Augmented Generation). It searches the live web, pulls snippets of information, and uses an LLM to summarize them.

The inaccuracies usually stem from three areas:

Fragmented Data Sources

Perplexity relies on the "Top 10" search results it finds in real-time. If your most recent press releases or product pages aren't indexed well, or if third-party review sites contain outdated information, the AI will synthesize that "stale" data as current truth.

Semantic Ambiguity

If your brand name is a common noun or shares a name with a different entity (e.g., a band, a chemical, or a small business in another country), the AI may suffer from entity confusion. This is a common hurdle in reputation management for modern brands.

Lack of Structured Data

LLMs are excellent at reading prose, but they are even better at parsing structured data. If your website lacks Schema markup or a clear "About" hierarchy, the AI is forced to guess, often leading to incorrect recommendations.

3. The Audit: How to Confirm What is Wrong

Before you can fix wrong recommendations about your brand in Perplexity, you need a comprehensive audit of your AI footprint.

Step 1: Query Mapping

Run a series of "Intent-Based" queries. Don't just search for your brand name. Search for:

• "What is the best [Category] tool for [Specific Use Case]?"
• "Compare [Your Brand] vs [Competitor]."
• "Does [Your Brand] offer [Specific Feature]?"

Step 2: Source Citation Analysis

Perplexity is unique because it provides footnotes. Click every single footnote in the "wrong" response. Where is the AI getting its information? You will often find the culprit is an old Reddit thread, a defunct tech blog, or an unoptimized LinkedIn profile.

Step 3: Sentiment and Accuracy Scoring

Create a spreadsheet to track:

• Accuracy: Is the factual data correct?
• Sentiment: Is the tone favorable?
• Visibility: Is your brand mentioned in the top 3 recommendations?

For enterprises managing hundreds of keywords, manual auditing is impossible. This is where Abhord’s AI Brand Alignment tools become essential, providing automated tracking of how your brand is perceived across generative engines.

4. Fixes, Corrections, and Content Updates

Once you have identified the source of the misinformation, it is time to take corrective action. This is the core of Perplexity SEO.

Update the "Source of Truth"

If Perplexity is citing an old page on your own site, update it immediately. However, if it’s citing a third-party site, you must reach out for a correction or publish "Counter-Content."

• Action: Publish a "State of the Brand 2024" page that explicitly lists features, pricing, and use cases in a clear, bulleted format.

Optimize for RAG (Retrieval-Augmented Generation)

To ensure brand accuracy, your content needs to be "AI-readable."

• Direct Answers: Use H2 headers that ask the question Perplexity is getting wrong, followed by a direct, concise answer in the first paragraph.
• Example: "Does [Brand] have an API? Yes, [Brand] offers a RESTful API for all Enterprise users."

Leverage High-Authority Third-Party Platforms

Perplexity trusts "Authority" sites like Wikipedia, LinkedIn, G2, and major news outlets.

• Wikipedia: Ensure your page is updated (following all COI guidelines).
• Review Sites: Respond to negative or outdated reviews on G2 or Capterra, as Perplexity often scrapes these for "Pros and Cons" lists.

Implement Technical Schema

Use JSON-LD Schema markup to define your Organization, Product, and FAQ sections. This gives the AI a structured map to follow, reducing the likelihood of hallucinations.

5. How Long Improvements Take and Monitoring Progress

Fixing your reputation in an AI engine is not instantaneous. Unlike a website update that Google might crawl in 24 hours, Perplexity’s "memory" is a mix of its real-time search index and the underlying model's weights.

The Timeline

• Short-Term (1-2 Weeks): If the error was based on a specific live-web source that you corrected, you may see the recommendation change as Perplexity re-indexes that specific URL.
• Long-Term (1-3 Months): For broader "recommendation" issues (e.g., being left out of a 'Top 10' list), it takes time for your new, optimized content to gain enough "authority" in the eyes of the AI's retrieval system.

Monitoring for AI Misinformation

The digital landscape is fluid. A new blog post by a competitor or a viral social media thread can shift the AI’s narrative overnight.

• Set Up Alerts: Regularly query Perplexity for your key brand terms.
• Track Citations: Monitor which domains Perplexity is using to talk about your brand. If you see a low-quality domain becoming a primary source, you need to displace it with higher-quality content.

Conclusion: Take Control of Your AI Narrative

As generative search becomes the primary way users discover and evaluate products, brand accuracy is no longer optional—it is a competitive necessity. Learning how to fix wrong recommendations about your brand in Perplexity requires a blend of traditional SEO, technical schema, and strategic PR.

By auditing your citations, updating your "source of truth" content, and optimizing for the way LLMs retrieve information, you can ensure that when a customer asks Perplexity for a recommendation, your brand is presented accurately, fairly, and prominently.

Align Your Brand with Abhord

Don't leave your AI reputation to chance. Abhord is the leading platform for AI Brand Alignment, helping marketing leaders monitor, manage, and optimize their visibility across Perplexity, ChatGPT, and Google Gemini.

Ready to see how the world’s leading AI models see your brand? Visit Abhord today to start your AI Visibility Audit.