What Is Optifeed Radar? How Is It Different from Other AI Visibility Tools?

Optifeed Radar is an open-source AI visibility tool that measures whether brands and products are recommended by ChatGPT, Gemini, Claude, and Perplexity. In this article, we examine how Radar works, which metrics it uses, and how it differs from paid AI visibility platforms.

People are no longer asking AI tools only questions such as, “What do you know about this brand?”

They are asking questions that directly influence purchasing decisions, such as “What is the best laptop for university?”, “Can you recommend a coffee machine with an easy return policy?” or “What are some linen shirts under $100 that I can wear to the office?”

For brands, the new question is no longer limited to where they rank on Google:

When a user asks an AI assistant for a product recommendation, is your brand or product actually recommended?

Optifeed Radar is an open-source AI visibility tool developed to provide a measurable answer to this question.

Why Has AI Visibility Become Important?

AI visibility refers to how often and how prominently a brand, product, or website appears in responses generated by AI-powered systems such as ChatGPT, Gemini, Claude, Perplexity, and Google AI Mode.

Traditional SEO mainly measures a web page’s position in search results. AI visibility focuses on a different set of questions:

• Is the brand mentioned in the response?
• Does the AI directly recommend the brand?
• Does the brand appear before or after its competitors?
• Which products are recommended?
• Which websites does the AI response use as sources?
• Is the brand described positively, neutrally, or negatively?

The importance of this area is not based solely on industry predictions.

According to research conducted by Bain & Company with 1,117 consumers, approximately 80% of users rely on AI summaries and other “zero-click” results for at least 40% of their searches. Bain estimates that this shift could lead to a 15% to 25% decline in organic web traffic. Read the Bain & Company research.

Despite this change, many brands are still not ready to measure their performance in AI search. In a 2026 Semrush study involving 481 marketing and SEO professionals, only 22% of respondents said they had fully integrated AI search with their traditional SEO efforts. Read the Semrush AI Search study.

Another study conducted by Ahrefs across 75,000 brands found that brand mentions on YouTube had a correlation of approximately 0.737 with AI visibility. Brand mentions across the wider web showed correlations ranging from 0.66 to 0.71.

Although these findings do not prove a direct cause-and-effect relationship, they indicate that AI visibility is not determined only by the SEO work carried out on a brand’s own website. Read the Ahrefs AI brand visibility research.

For this reason, brands need to do more than produce content. They also need to regularly check how visible they actually are across AI platforms.

What Is Optifeed Radar?

Optifeed Radar is an open-source AI visibility tool that measures whether brands and specific products are recommended in purchase-oriented responses generated by ChatGPT, Gemini, Claude, and Perplexity.

Radar analyzes a brand’s website, creates buyer questions that are relevant to the business, and sends those questions to the configured AI engines. It then reports whether the brand or product appears in the response, where it is positioned, which competitors are recommended, and which sources are used.

The main question the tool attempts to answer is simple:

When a user asks AI what to buy or where to buy it, do your brand and products appear in the answer?

The main AI engines supported by Radar include:

• OpenAI
• Google Gemini
• Anthropic Claude
• Perplexity

Optifeed Radar is open source, released under the MIT License, and runs on the user’s own computer. There is no analysis backend hosted by Optifeed. Requests to AI engines are made through the user’s own API keys.

How Does Optifeed Radar Work?

Radar’s workflow consists of five main stages.

1. It Analyzes the Website and Business Type

Radar first reviews the brand’s website and tries to understand what the business sells.

It also determines whether the business is a brand that manufactures its own products or a retailer that sells products from multiple brands.

This distinction matters.

For a brand that manufactures its own products, a recommendation question such as “What are the best gluten-free snacks?” may be relevant. For a retailer that sells products from different brands, a seller-oriented question such as “Where can I buy an acoustic guitar?” is more likely to produce an accurate result.

When the wrong type of question is used, retailers may be compared with product manufacturers, producing misleadingly low scores. Radar aims to reduce this problem by adjusting the question structure according to the business type.

2. It Generates Purchase-Oriented Questions

The system generates unbranded buyer questions based on the brand’s industry and product categories.

For example, it might generate the following questions for a laptop brand:

• What are the best laptops for university students?
• Which computers would you recommend for graphic design?
• What are some lightweight laptops with long battery life?

Questions that directly include the brand name are not used in the main visibility score. This is because the brand would already be expected to appear in the response to a question such as “Is Brand X reliable?”

Unbranded questions show whether the AI system recommends the brand independently, without being prompted to mention it. Branded questions are reported separately as part of reputation and sentiment analysis.

3. It Sends the Questions to Real AI Engines

The generated questions are sent to the OpenAI, Gemini, Claude, and Perplexity APIs configured by the user.

Radar does not only examine records stored in a search index. It collects responses generated by the selected AI engines at the time of the analysis.

There may be two different types of responses:

• Parametric responses based on the model’s existing training data
• Grounded responses based on current sources retrieved through web search

Radar also records whether the AI engine actually performed a web search and whether it provided citations.

4. It Calculates Visibility Metrics

The following metrics are analyzed across the collected responses:

• The number of responses in which the brand appears
• Average appearance position
• Sentiment toward the brand
• How frequently competing brands appear
• Share of voice
• Sources used by the AI
• Whether specific products are recommended

5. It Stores the Data Behind the Results

Optifeed Radar does not simply display a single number such as “AI Visibility Score: 62.”

The following data behind each result can be stored locally:

• The question that was used
• The raw response generated by the AI engine
• The section in which the brand appeared
• The detected position
• Competing brands
• Citations and source domains

This allows users to examine why a score increased or decreased. Changes between two separate analyses can also be reviewed through snapshot comparisons.

Does Optifeed Radar Only Measure Brand Visibility?

No. One of Optifeed Radar’s key differences is that it can also measure the visibility of specific products in AI-generated responses.

The user defines the products they want to analyze. Radar then creates category-based and purchase-oriented questions for each product.

The analysis can answer questions such as:

• Was the product recommended by the AI?
• If it was recommended, what position did it appear in?
• For which need or use case was it shown?
• If the product was not shown, which competing products were recommended?
• Which products from the same brand are more visible?

For example, a brand may have a high overall visibility score while one of its newly launched products never appears in purchase recommendations.

Looking only at an overall brand-level score may therefore not be enough for product teams. Product-level analysis can provide more actionable insights, particularly for e-commerce brands with large catalogs.

The product visibility feature in Optifeed Radar is currently in beta, and products must be defined manually by the user. There is currently no automatic product feed or catalog import process.

How Is Optifeed Radar Different from Other AI Visibility Tools?

Not all AI visibility tools follow the same usage model.

Platforms such as Peec AI, OtterlyAI, Profound, and Ahrefs Brand Radar are primarily web-based SaaS products. Users create an account, define the prompts they want to monitor, and review the results through a ready-made dashboard.

Optifeed Radar takes a different approach.

It Is Open Source

Radar’s source code can be reviewed, modified, and adapted to different workflows through GitHub.

This is particularly valuable for technical teams that want to audit the methodology or integrate the measurement process with their internal systems.

It Runs Locally

Radar’s results are stored on the user’s computer. API keys are not sent to a system hosted by Optifeed.

However, because analysis questions and responses pass through the selected AI provider’s API, that provider’s own data and privacy policies still apply.

It Is Free and Does Not Require a Subscription

Optifeed Radar does not require a monthly software subscription.

The AI-readiness audit works for free without making any AI calls. For visibility checks, users provide their own AI provider API keys and only pay the resulting API usage costs.

In Optifeed Radar tests conducted on July 20, 2026, a quick check involving eight prompts across four AI platforms cost approximately $0.41–$0.46 in total. Grounded checks may cost more because of additional web search charges.

These figures are not fixed prices. They are example measurements that may vary depending on the model used and the provider’s pricing.

Its Methodology Is Transparent

It is not always clear how a single score is calculated on AI visibility platforms.

Optifeed Radar explains how appearance rate, position, sentiment, and retrieval weighting affect the score in its published methodology documentation.

This allows users to examine not only the final result, but also how that result was produced.

It Shows Results Together with Raw Responses

Radar connects every detected appearance or absence to the relevant prompt and AI response.

This approach goes beyond telling users that “our score has decreased.” It shows which question caused the visibility loss, on which AI engine it happened, and which competitor appeared instead.

It Can Be Run by AI Agents

Radar is not limited to being a CLI tool.

With Agent Skill and MCP support, it can be operated through Codex, Claude Code, Cursor, and compatible AI agent environments. An agent can be asked to run a site audit, check product visibility, or explain the changes between two snapshots.

Optifeed Radar vs. Other AI Visibility Tools

The features and pricing information in this comparison were checked against the tools’ official websites on August 7, 2026. Plans and supported platforms may change over time.

Can Optifeed Radar Replace Paid Platforms?

Not in every use case.

Optifeed Radar and SaaS-based AI visibility platforms address different needs.

Optifeed Radar may be more suitable for:

• Teams measuring AI visibility for the first time
• Brands that want to keep their data within their own environment
• Developers looking for an open-source solution
• GEO specialists who want to inspect the methodology and raw responses
• E-commerce teams that want to run experimental product-level visibility checks
• Technical teams that want to connect the analysis to their own agent, CI, or reporting systems
• Businesses that prefer usage-based API costs over a monthly SaaS subscription

SaaS platforms may be more suitable for:

• Automated daily prompt monitoring
• Ready-made web dashboards
• Managing multiple brands and countries from one interface
• Team roles and user permissions
• Scheduled reports
• Large historical prompt databases
• Usage that does not require technical setup

For this reason, it would not be accurate to describe Optifeed Radar as a free, one-to-one replacement for paid platforms.

Radar’s strength lies in open-source, local, auditable, and product-focused AI visibility measurement. Paid SaaS platforms are generally more comprehensive when it comes to continuous monitoring and operational convenience.

How Should Optifeed Radar Results Be Interpreted?

AI engines do not always provide the same answer to the same question.

The model being used, whether web search is enabled, the date of the response, language, location, and the phrasing of the question can all affect the result.

For this reason, the score generated by Optifeed Radar should not be treated as a definitive measure of “AI market share.”

Radar’s published methodology also clearly states that the score is a measurement based on a limited sample of prompts. The buyer questions are not taken from a panel of real user prompts. Instead, they are generated from the business profile created by analyzing the brand’s website. Users can review and edit the prompt set.

The right approach is to:

• Avoid assigning too much importance to a single score.
• Review the questions for which your brand did not appear.
• Identify the competitors that were recommended.
• Analyze the sources used by the AI engines.
• Run the same prompt set again after making changes.
• Evaluate results across multiple analysis runs.

Radar should not be used to claim that “our AI visibility is exactly 62%.” It should be used to identify visibility gaps, product-level issues, and competitive opportunities.

What Is the Difference Between AI Visibility and an AI-Readiness Audit?

Optifeed Radar can address two different problems.

An AI visibility check examines whether AI engines actually recommend the brand and its products.

An AI-readiness audit evaluates whether the website is technically accessible and understandable to AI systems.

The free audit command checks the following areas:

• AI crawler access and robots.txt
• llms.txt
• Schema.org and structured data
• Meta title and description
• Sitemap
• Core AI-readiness signals

The audit does not make paid calls to any AI provider and does not require an API key.

However, being technically ready does not automatically result in high AI visibility.

A website may have a correct schema structure while the brand is not sufficiently known in AI responses or is not mentioned often enough by trusted sources. Conversely, a strong brand may be frequently recommended by AI systems despite having technical shortcomings.

Readiness and visibility should therefore be evaluated together.

Why Is It Important for E-Commerce Brands?

Brand visibility alone is not enough in e-commerce.

A user may already know the brand, but an AI assistant may still prioritize competing products when responding to a specific product recommendation request.

For example, a fashion brand may be frequently mentioned in general questions. However, its relevant product may not be recommended for a query such as “a wrinkle-resistant midi dress for a summer holiday” if the product data does not include information about the fabric, use cases, or care instructions.

This is where AI visibility measurement and product data optimization complement each other.

Brands first need to identify which products and areas of customer need are not visible. They can then improve their product titles, descriptions, attributes, images, and trust signals.

To learn more about this preparation process, you can read the following Optifeed articles:

• AI-Powered Shopping Experiences: A New Era of Product Discovery for Brands
• Why Product Feeds Are Becoming the Core Layer of AI Shopping
• How to Build an AI Shopping-Ready Product Feed

These articles explain in more detail how AI platforms evaluate products and why structured product data matters for visibility.

How to Use Optifeed Radar

Optifeed Radar can be run from the terminal through npx in an environment using Node.js 20 or later.

To run the free site audit, which does not require an API key:

npx optifeed-radar audit yourbrand.com

To check brand visibility after configuring an API key for at least one AI engine:

npx optifeed-radar check yourbrand.com

To check specific products:

npx optifeed-radar shopping yourbrand.com --products "Product A, Product B"

Analyses can be run through the terminal and exported as JSON or standalone HTML reports. Radar can also be connected to AI agent workflows through its Agent Skill and MCP options.

For the latest installation commands, features, and methodology, visit the Optifeed Radar GitHub page.