New in AI Brand Visibility: 5 Updates to Help You Win in AI Search

Now that the year’s properly underway, here’s a quick snapshot of what we shipped through the end of January for our AI Brand Visibility tools, and how it helps you show up (and stand out) in AI search.

These updates give you a clearer view of how your brand performs across AI models and markets, along with faster ways to build campaigns and turn the data into an AEO plan you can actually execute.

If you’ve been busy, this is your practical recap.

Smarter AI visibility across models and languages

We’ve expanded your view beyond one model or language, so you can see how your brand shows up in the places your audience is actually using.

See real AI conversations in Gemini, Perplexity, and AI Mode

Each model behaves differently. Some cite sources more often. Some surface more competitor mentions. Others focus on a smaller set of brands.

Now you can compare performance across ChatGPT, Gemini, Perplexity, and AI Mode in one place.

What this means for you:

• Compare visibility by model to spot where you’re winning and where you’re getting squeezed out
• See citation patterns to understand what each model trusts (and why)
• Find model-specific opportunities to improve how your brand shows up

We’ve also added an LLM comparison chart so you can benchmark your visibility against competitors across models at a glance.

Multi-language support for local markets

AI results change by language. Your AEO strategy should too.

You can now track prompts and AI answers in local languages, so you can:

• Make better content calls based on what users ask in each region
• Pick up demand you’ll miss in English, especially in long-tail prompts
• Build local strategies instead of copying and pasting one global playbook

This is especially useful if you’re managing multiple regions, or you’re trying to grow outside English-first markets.

Stronger brand tracking in AI

Multi-brand variations

Most companies don’t operate under one name. You’ve got sub-brands, product names, and regional variations.

With multi-brand variations, you can track every version of your brand and pull it into one view.

That means:

• Fewer missed mentions
• Cleaner brand reporting
• More accurate visibility across AI-generated answers
• More confidence that your numbers match reality

This is a big win for global brands, retailers, and teams with multi-brand portfolios.

More accurate topic discovery

AI-suggested topics

Choosing what to track can slow everything down.

With AI-suggested topics, campaign setup is easier, and your topic list is smarter.

You can:

• Get context-rich topic suggestions that match your domain
• Find more relevant topics tied to real use cases
• Start with more options, without being boxed into a preset list

Think of it as a faster way to build a topic strategy that doesn’t miss the obvious (or the high-intent).

Why this matters for your AEO strategy

AI search isn’t one engine, one market, or one set of rules. These updates help you stay accurate when the outputs change by model, language, and brand naming.

With these updates, you can:

• Track visibility across models and markets without stitching together separate views
• Trust your reporting with cleaner brand matching and fewer gaps
• Move faster from insight to action, with better topic discovery and campaign setup
• Build an AEO strategy you can maintain, even as models and behaviors shift

What’s next?

We’ll keep expanding coverage, sharpening the insights, and making it easier to turn AI visibility into decisions your team can act on.

Let’s make 2026 the year your brand show up everywhere AI answers. 

by Rice Tong

Product Marketing Manager

Rice is a Product Marketing Manager at Similarweb, focused on the evolution of search and AI. She loves shaping stories that make complex ideas feel human and relatable.