SEMRush AI Toolkit vs Waikay
AI search is rapidly reshaping how people discover brands. Instead of relying solely on Google search results, consumers are asking ChatGPT, Gemini, and Perplexity AI for recommendations, comparisons, and answers. This shift means businesses can no longer afford to just monitor their SEO rankings—they need to know how AI systems perceive and present their brand.
That’s where AI visibility tools come in. Two notable players are SEMrush’s AI Toolkit and Waikay. This article compares Semrush AI vs Waikay features.
- SEMrush AI Toolkit offers a broad overview: market share, sentiment, competitive comparisons, and visibility by platform. It’s geared toward answering high-level questions like “Where do we stand versus competitors?”
- Waikay is more of a deep dive: it monitors entity patterns, prompt-level triggers, and narrative evolution over time. Instead of just showing if you’re mentioned, it helps you understand how your brand’s story is being formed inside AI systems.
| Feature | SEMrush AI Toolkit | Waikay |
|---|---|---|
| Visibility Overview | ✅ | ✅ |
| Competitor Research | ✅ | ✅ |
| Prompt Research | ✅ | ✅ |
| Brand Performance (share of voice, sentiment, narratives) | ✅ | ✅ |
| Prompt Tracking | ✅ | ✅ |
| AI Search Site Audit | ✅ | ❌ |
| Citation Data (trace sources, fix broken refs) | ❌ | ✅ |
| Entity-Level Topic Reports | ❌ | ✅ |
| Entity-Based Content Plans | ❌ | ✅ |
In this post, we’ll break down SEMrush’s key features one by one and compare how Waikay approaches the same challenges. By the end, you’ll see where each tool shines, and why Waikay’s entity-focused approach is worth watching.
Brand Visibility/performance reports
Brand Visibility per Prompt
Both SEMrush and Waikay track visibility on specific prompts. You can choose the queries that matter most to your brand and measure how often you appear in AI-generated answers compared to competitors. SEMrush visualizes this with its Share of Model feature, showing the percentage of space your brand occupies in responses and how that share shifts over time.
Waikay works in a similar way but adds an extra layer of depth. Each response is broken down into entities—the themes, attributes, and associations tied to your brand. Over time, this creates a detailed entity profile, with heat maps that let you see not just how often you’re mentioned, but how you’re being framed in the AI’s output.
In short:
SEMrush: Great at giving a clean, high-level benchmark of visibility per prompt.
Waikay: Stronger when you need to understand the context and attributes behind those mentions.
Brand Performance
This is a unique feature of SEMrush’s AI Toolkit. Instead of only tracking your chosen prompts, SEMrush generates a wide pool of “common queries,” runs them through LLMs like ChatGPT and Google AI Mode, and then analyzes the responses. It looks at whether your brand is mentioned, how it’s described, and what sentiment or attributes are attached. From there, it produces recommendations to refine your positioning.
The benefit is a month-by-month signal of overall brand performance across AI search—a broader view than prompt-level tracking.
But the limitation is accuracy. The queries SEMrush uses dont seem to be based on real search data; they’re AI-generated. That means smaller brands in niche spaces might see reports built on questions nobody is actually asking. It’s an ambitious feature, but one where the reliability is debatable.
Waikay doesn’t currently offer a Brand Performance feature.
Citation tracking
When it comes to AI tracking, actionable data is still emerging—but one signal we can and should pay close attention to is how large language models (LLMs) cite sources. Yes, citations can be messy: broken links, misattributions, even outright hallucinations. But their presence alone reveals something deeper.
These citations show how LLMs are interpreting your site architecture and URL structures. If a model consistently references certain pages, it’s a sign those URLs are semantically clear and structurally sound—at least to the AI. That’s a visibility signal worth watching.
Citation tracking is one of the clearest points of difference between the two tools. SEMrush’s AI Toolkit doesn’t seem to collect citation data across your competitive landscape, or they at least do not spotlight this as a core feature. That leaves you with a fragmented picture—you can see what’s being said, but you can’t trace it back to where those claims are coming from. Without that layer of source tracking, it’s harder to separate signal from noise, or to dig into whether the mentions driving sentiment are rooted in solid references or in misleading AI-generated content.
Waikay, by contrast, treats citation tracking as a core feature. It separates competitor sources from knowledge sources: competitor sources come from prompts that explore your broader landscape, while knowledge sources are specific citations tied to your brand. That split makes analysis far more practical. For instance, you can instantly spot broken references or 404s, turning what would otherwise be junk data into clear opportunities for content updates or outreach. Waikay also stores every reference inside a dedicated competitor research section, letting you drill down to the individual AI replies behind a trend. That means you can isolate the exact citations that might be causing misinformation, or uncover review sites and third-party mentions worth connecting with.
The result is that Waikay doesn’t just give you patterns—it gives you a roadmap. Instead of just observing the conversation around your market, you can trace it back to the underlying sources, validate them, and act on them.
AI Sentiment Tracking
SEMrush’s sentiment analysis breaks mentions into positive, neutral, and negative. It also highlights the drivers behind them—for example, “stylish design” might come through as a positive, while “limited selection” is flagged as a negative. It’s a solid way to take the temperature of consumer perception. It’s also presented in a stylish way and allows semrush to generate content strategies based off the results.
The problem is that sentiment in AI search can feel surface-level. A glowing AI-generated recommendation isn’t useful if the facts are wrong.
That’s where Waikay takes a different stance. Instead of scoring outputs as positive or negative, it tracks whether AI descriptions of your brand are factually correct. In AI-driven discovery, this distinction is critical—because misinformation, even when “positive,” can damage trust and lead customers astray.
This is where Waikay stands apart not just from SEMrush, but from many tools in the market. While SEMrush compiles an understanding of positives and negatives as told by AI, Waikay focuses on the actual facts being stated—and on adjusting those facts at the core level of how your brand is understood.
And here’s the kicker: to even get a “positive vs. negative” breakdown, an AI has to be prompted to look for it. That usually means pulling in odd lists from scattered review sites and stitching together a narrative that may not reflect reality. Chasing that kind of data can become a futile exercise in managing whacky, inconsistent lists of online reviews.
Waikay avoids that trap by anchoring its analysis in core beliefs and factual associations. Every AI response is broken down into discrete facts—what the AI says about you. From there, Waikay gives you a simple way to flag inaccuracies and feed them back to your team.
It’s a steady, manageable approach to reputation: focus on ensuring the facts are right, rather than burning resources trying to keep up with fickle online reviews or sentiment swings.
Feature | SEMrush AI Toolkit | Waikay |
Approach | Labels mentions as Positive, Neutral, Negative. Highlights drivers (“stylish design” vs “limited selection”). | Tracks factual accuracy: Are AI descriptions correct? |
Strength | Quick temperature check of consumer perception. | Protects reputation by correcting misinformation/hallucinations. |
Limitation | Sentiment can be misleading—“positive” but factually wrong still hurts trust. | Narrower, but grounded in core facts, not fickle review swings. |
Takeaway | Tells you if people are “happy/unhappy.” | Ensures what’s being said is true, consistent, and defensible. |
The takeaway: SEMrush tells you if people are “happy or unhappy.” Waikay ensures what’s being said about you is true, consistent, and defensible—a far stronger foundation for brand reputation in the AI era.
Pricing and Positioning
One of the biggest differences between the two tools isn’t methodology but cost. The SEMrush AI Toolkit is an add-on—you need a full SEMrush subscription first, then the price is $99 per one domain. For brands and agencies already embedded in that ecosystem, it makes sense. It integrates neatly with SEMrush’s wider SEO suite and digs deeply into your existing domain. But it comes at a premium, which makes it hard to justify if AI visibility is all you need.
A semrush plan at $99 per month includes
- 1 project
- 1 domain for Brand Performance analysis
- 25 prompts for Prompt Tracking
- AI Search Checks in Site Audit for up to 100 pages
- Unlimited projects
- Unlimited domains
- 30 individual prompts, 120 api calls
- Build up to 30 GEO Action Plans
- 13 Languages. 40+ Countries
Waikay takes the opposite approach. It’s designed for businesses who want to track multiple domains, or who simply don’t want to spend heavily on a single one. It starts with a free tier, and its lowest-cost paid plan comes in at US$24.95/month. That makes it accessible for smaller teams and anyone who wants long-term monitoring without adding another hundred-plus dollars a month to their stack.
Opportunities: Turning Data Into Action
The real value of AI visibility tracking comes from what you do next. SEMrush approaches this at a fairly surface level with its AI Opportunities feature. By scanning the questions it tracks and identifying holes in how brands appear in responses, it generates recommendations that help plug the gaps. The result is useful for spotting where you’re absent, but the actions tend to stay broad and generic.
Waikay, on the other hand, takes a far more granular path. Instead of treating opportunities as one-size-fits-all fixes, it encourages users to build action plans around specific entities. That means you can keep things broad when you want direction, or go much deeper—spending credits to unlock detailed, entity-level insights. The effect is that you’re not just reacting to missing mentions, you’re actively shaping the narrative of how AI systems describe your brand.
SEMrush AI Toolkit | Waikay | |
How it works | Flags gaps in AI answers and suggests broad fixes. | Anchors improvements to specific entities, building tailored action plans. |
Depth | Surface-level, generalised opportunities. | Granular: can stay broad or dive deep per entity. |
Flexibility | Largely one-size-fits-all. | Lets you decide how far to explore. |
End result | You see where you’re missing. | You learn how to reshape your AI-driven brand narrative. |
The Verdict: Why Entity Patterns Are the Future of AI Visibility
Both tools have their place.
- SEMrush AI Toolkit is ideal for quick overviews: Who’s visible, how sentiment breaks down, and where you stand across platforms. It’s broad, polished, and useful for benchmarking.
- Waikay is designed for depth: It doesn’t stop at visibility. It shows how AI is building your brand narrative, prompt by prompt, entity by entity. It’s also priced for long-term tracking, making it more accessible for businesses that want to monitor AI search continuously rather than pay premium rates per domain.
In a world where AI-generated content is shaping consumer decisions, it’s not enough to know whether you’re visible—you need to know how you’re being represented. That’s why Waikay’s focus on entity patterns and factual accuracy makes it the smarter choice for brands who care about long-term presence in AI-driven discovery.
Feature | Waikay | Semrush AI Toolkit |
Focus | Entity patterns in AI outputs, accuracy of information, prompt tracking, long-term visibility monitoring. | AI visibility as part of a wider SEO/content strategy: competitor benchmarking, sentiment, prompt analysis. |
Depth vs Breadth | Narrower focus but deep—great at spotting misinformation, hallucinations, and broken citations. | Wider toolkit covering visibility, sentiment, competitor trends, and content recommendations. |
Pricing | Affordable: starts at ~US$20/month, with free tier available. | Premium: often cited around US$99/month per domain, on top of Semrush subscription. |
Best Fit | Businesses that want to protect reputation, correct misinformation, and track long-term patterns without breaking the bank. | Larger brands or agencies already invested in Semrush, who want to fold AI visibility into broader marketing workflows. |
