Artificial intelligence has reshaped the discovery funnel. Traditional SEO still matters, but users increasingly rely on AI assistants such as ChatGPT, Gemini, and Perplexity to answer questions directly. The result is a new challenge: your brand can either be part of those answers, or be completely invisible.
This article explains how to approach AI analytics and discovery tracking from a technical perspective. We’ll cover how AI search visibility works, which data points matter, and how you can capture these insights programmatically. By the end, you’ll understand how to implement your own monitoring pipeline and feed insights back into your marketing and product strategies.
From SEO to AI Discovery
SEO has traditionally focused on:
- Ranking for keywords
- Driving clicks from search results
- Measuring impressions and CTR in Google Analytics or Search Console
AI discovery works differently:
- Assistants generate direct answers, often without linking back.
- Users see summarized context, with only a few external references.
- Visibility depends on whether the model recognizes your brand as a trusted entity.
This requires a new layer of measurement: AI analytics.
What Is AI Search Analytics?
AI analytics refers to monitoring how and when AI systems mention your brand. Instead of tracking pageviews, you’re tracking:
- Brand mentions inside AI-generated answers
- Query context: the type of questions that trigger your brand
- GEO and language distribution
- Competitor presence: how often rivals are cited versus you
This goes beyond traditional SEO. It’s about making sure you’re present inside the systems people are increasingly trusting for decision-making.
Why It Matters Now
AI assistants are quickly becoming embedded into devices, productivity suites, and even browsers. If your brand isn’t being surfaced:
- You’re losing discovery opportunities to competitors
- Customers will make decisions without ever seeing your website
- Entire regions may never encounter your brand in AI answers
Visibility in AI search is no longer optional. It’s a competitive necessity.
Core Features to Track
When setting up AI analytics, prioritize these data points:
Real-time brand mentions
Track how often your company is cited in ChatGPT, Gemini, Perplexity, or other AI engines.GEO and language breakdown
Visibility differs by country and language. A user in the Netherlands may get different answers than one in the US.Competitor benchmarking
Compare your mentions against competitors in similar queries.Contextual analysis
Understand which types of prompts include your brand. For example: “best workflow automation tool” vs “top AI analytics platform.”Content gaps
Identify where competitors are mentioned but you are not. This informs future articles and landing pages.
How to Appear in AI Search
It’s not random when your brand gets mentioned. Developers and marketing teams can improve visibility by focusing on:
Structured, high-quality content
AI systems pull from trusted sources. Technical documentation, long-form guides, and case studies all increase inclusion.Entity consistency
Make sure your company name, description, and details are consistent across your site and knowledge bases.GEO optimization
If you want visibility across regions, publish in multiple languages and include country-specific signals.Authority building
Citations, third-party mentions, and strong references build trust signals that feed into AI datasets.
For more on this, see our workflow automation guide.
Technical Implementation: API to Airtable
AI analytics platforms increasingly provide APIs that allow teams to query visibility data directly. A common architecture is:
- Scheduler (cron, or automation tool like Make)
- API Request to the analytics endpoint (e.g., mentions, competitors, geo)
- Iterator / Parser to split response arrays
- Storage in a structured format such as Airtable or PostgreSQL
- Dashboard to visualize trends and share with stakeholders
Example flow
- Endpoint:
/mentions - Parameters:
brand,start_date,end_date - Response: JSON array of mentions
Store each mention in Airtable with fields such as Date, Brand, Query, Context, Count. Do the same for GEO and competitors.
Once data is flowing, you can build views:
- Weekly trends of AI mentions
- Geo distribution by country
- Competitor benchmarks across queries
This creates a living “AI Search Console” for your brand.
Developers: How to Start Testing
If your analytics platform exposes Swagger/OpenAPI documentation, you can:
- Inspect available endpoints (mentions, geo, competitors).
- Use “Try it out” to test with your API key.
- Note required parameters and expected response fields.
- Script simple requests with
curl,fetch(JS), orrequests(Python). - Pipe results into your own datastore.
For example, a daily job could run:
-
Call
/mentionswithdate_range=today - Store in Airtable via their REST API
- Send Slack notification if competitor mentions exceed your own
Strategic Benefits
By implementing AI analytics, teams gain:
- Clarity: where and when your brand is visible in AI answers
- Insights: what content and keywords drive mentions
- Global reach: which regions need stronger presence
- Competitive edge: proactive response to rivals dominating queries
This directly influences your content roadmap and marketing strategy.
AI Analytics Roadmap
To summarize, a practical roadmap looks like this:
Monitor AI mentions
Collect daily or weekly data from the API.Analyze context and GEO
Understand where and how your brand is visible.Optimize content
Target the gaps with new guides, case studies, or landing pages.Measure improvements
Track changes in visibility and share with stakeholders.
This cycle ensures continuous growth in AI search visibility.
Final Thoughts
AI discovery is the new SEO battlefield. Waiting means competitors gain presence in AI results while you remain invisible.
By building an AI analytics pipeline, you can:
- Monitor visibility in real-time
- Adjust strategies based on data
- Scale globally with GEO insights
Don’t wait until AI search is saturated. Build your visibility today.
Call to Action
Want to set up your own AI analytics pipeline?
Contact Scalevise and we’ll help you design, implement, and scale a solution tailored to your business.
Top comments (4)
For those experimenting with APIs: connecting AI analytics endpoints to Airtable or PostgreSQL gives a really clean pipeline.
The GEO angle is spot on. I tested the same query in the US and Germany, and the AI outputs were completely different. Localization might be the hardest part here.
Exactly. What you noticed is a key reason why AI analytics needs to account for GEO differences. AI assistants often use region-specific training data or source sets, which means two people asking the same thing can get totally different answers. That’s why tracking mentions per country is so valuable it shows you where your brand is already visible, and where you still need to localize content.
This is exactly what I needed to hear (without even knowing it) 😅 I’ll keep this in mind