LLM Mentions API: heeb.ai Quick Walkthrough

Overview

Generative AI has redefined how people discover brands. Instead of relying only on traditional search engines, users now ask large language models (LLMs) such as ChatGPT, Gemini, and Claude for recommendations. This shift has created a new layer of visibility that many brands are missing, mentions within AI-generated answers.

The heeb.ai LLM Mentions API makes it possible to track those mentions automatically. It allows marketers, developers, and data analysts to monitor how their brand or product appears in model-generated outputs, evaluate sentiment, and gather citations for deeper analysis. In this walkthrough, you’ll learn how to set up and use the API, interpret responses, and unlock its commercial potential for brand monitoring and Answer Engine Optimization (AEO).

1. Why LLM Mentions Matter

Traditional SEO measured ranking and traffic. Now, AI answers shape decisions directly in conversation. A 2025 study by Search Engine Land found that brands listed on Google’s first page appear in ChatGPT responses over 60% of the time (source).

The challenge? LLM visibility is dynamic because models pull from multiple data sources, user contexts, and embeddings. Without structured tracking, brands risk missing out on where and how they’re mentioned, or how those mentions affect perception.

That’s where heeb.ai’s LLM Mentions API helps. It provides structured visibility measurements, identifies sentiment, and surfaces references used by the models so you can manage your reputation intelligently.

2. How the heeb.ai LLM Mentions API Works

At its core, the LLM Mentions API lets you query multiple large models programmatically with one prompt, detecting whether your brand or entity appears, in what context, and with what tone.

Each request goes through three main steps:

1. POST a new query specifying the models, entity, and prompt.
2. Retrieve results via a job_id using a GET request.
3. Analyze structured JSON output containing visibility scores, mention details, sources, and sentiment indicators.

The API currently supports a range of models including OpenAI GPT versions, Google’s AI Mode, and others.

3. Setting Up and Authenticating

1. Create a heeb.ai account and go to /dashboard/keys to generate your API key.
2. Include it in the header for every request:

x-api-key: YOUR_API_KEY

4. Submitting a Query

Here’s how a basic query looks:

{
  "models": ["openai/gpt-5.2-chat-latest", "google/ai-mode"],
  "prompt": "what is the best football boots in 2025?",
  "entity": {
    "name": "Nike",
    "url": "https://nike.com"
  },
  "location": "CA"
}

A successful POST returns a job_id that begins asynchronous processing. Most responses are ready within a minute or two.

5. Viewing Results

Once your job completes, retrieve results:

GET /api/query/{job_id}

You’ll receive a detailed JSON response. Below is what a typical result looks like (trimmed for readability):

{
  "prompt": "what is the best football boots in 2025?",
  "location": "CA",
  "entity": {
    "name": "Nike",
    "url": "https://nike.com"
  },
  "results": [
    {
      "model": "openai/gpt-5.2-chat-latest",
      "status": "success",
      "content": "There isn’t one single best football boot for everyone in 2025 ...",
      "sources": [
        "https://www.gq.com/story/willy-chavarria-adidas-ss26",
        "https://www.goal.com/en-ca/lists/best-mens-soccer-cleats/blt98e200f2a25b257e",
        "https://www.fourfourtwo.com/buying-guide/best-football-boots"
      ],
      "citations": [
        {
          "url": "https://www.goal.com/en-ca/lists/best-mens-soccer-cleats/blt98e200f2a25b257e",
          "title": "The best soccer cleats you can buy in 2025 | Goal.com Canada"
        },
        {
          "url": "https://www.fourfourtwo.com/buying-guide/best-football-boots",
          "title": "Best Football Boots for 2025 | FourFourTwo"
        }
      ],
      "mentions": [
        {
          "entity": { "name": "Nike", "product": "Tiempo Legend 10 Elite" },
          "position": 3
        }
      ],
      "mentioned": true,
      "sentiment": "positive"
    }
  ],
  "scores": {
    "visibility": 100,
    "sentiment": 1
  }
}

Each response bundle gives you:

• mentioned: Whether your entity appeared in the model’s response.
• sentiment: The tone detected for your brand or product (positive, neutral, or negative).
• sources: The underlying data sources or domains that the model used when generating its answer.
• citations: Reference URLs explicitly cited by the model in the output.
• scores: Aggregated sentiment and visibility metrics calculated across all queried models.

This format is ideal for database storage, dashboards, or automation workflows.

6. Commercial Use and Workflow Integration

Build Real-Time Brand Dashboards

Combine heeb.ai’s API with analytics tools like Looker, Grafana, or Google Data Studio to visualize your AI visibility trends week by week.

Automate Alerts and Reports

Link the API with services such as Zapier or n8n to trigger slack or email notifications when sentiment dips or mentions drop below a defined level.

Identify Content Opportunities

The API can expose missing mentions or unlinked citations, helping your marketing team find content topics to restore brand presence in generative results.

Support Competitive Intelligence

Track how frequently rival brands are referenced by each model and cross-compare sentiment results for benchmarking.

7. Example Use Case

A footwear brand can schedule daily queries like:

{ "prompt": "best football boots 2025", "entity": {"name": "Nike", "url": "https://nike.com"} }

Tracking results over time reveals whether Nike stays visible in AI responses and how sentiment changes after a new release. If positivity drops or Adidas begins appearing higher in model mentions, your marketing team can respond immediately.

8. Getting Started Quickly

If you’re ready to test it yourself:

• Sign up at heeb.ai
• Generate your key in under a minute
• Run your first job with this sample cURL snippet:

curl -X POST "https://api.heeb.ai/api/query" \
-H "Content-Type: application/json" \
-H "x-api-key: YOUR_API_KEY" \
-d '{
  "models": ["openai/gpt-5.2-chat-latest", "google/ai-mode"],
  "prompt": "best project management tools 2025",
  "entity": {"name": "Notion", "url": "https://www.notion.com"},
  "location": "US"
}'

You’ll receive structured JSON back that looks just like our earlier example, perfect for analysis or AI pipeline automation.

FAQs

What is the LLM Mentions API?

It’s an API that allows you to track brand or entity mentions, sentiment, and citations within LLM-generated responses across multiple models.

How do I use it?

Authenticate with your API key, submit a POST query with your prompt and entity, and retrieve structured JSON results through the job_id.

What insights do I get?

Visibility metrics, sentiment analysis, and source URLs, all formatted for easy integration into dashboards or automation systems.

Can I monitor multiple brands simultaneously?

Yes, you can specify multiple entities and prompts programmatically to create comparative brand analytics workflows.

Why use heeb.ai for this?

Because it consolidates data from several LLMs, saving you from manual querying and normalization while giving you clear, actionable insights about how AI perceives your brand online.

Start monitoring your brand visibility across AI models today with the heeb.ai LLM Mentions API.