Waikay Review: Checking AI Facts About Brands and Correcting Hallucinations

What does Waikay check?

According to Waikay's help centre and product pages:

• Facts: the statements each AI model makes about your brand, products, services and key attributes, drawn from its Brand Overview and Topic reports. You review each fact and choose Check if it is accurate or Flag if it is not. Facts can be exported as a CSV.
• AI Knowledge Score: a 0 to 100 score that compares a knowledge graph built from your website's most important pages with one built from AI models' answers about your brand. A low score means the models' picture of you diverges from what your site says.
• Topic reports and action plans: how well models understand your brand on specific topics, where knowledge is thin, and steps to improve it.
• Sources: which pages models draw on, split into sources that shape how they understand your brand and sources cited when they answer competitive questions.
• Prompt tracking: visibility across models for prompts you choose, by scraping the consumer interfaces or through APIs.

How does Waikay tell training data from live search?

This matters for fixing errors, because the two need different remedies. Waikay labels its reports:

• Training data (TD): what a model says from its pre-trained knowledge, without searching. Waikay lists ChatGPT, Gemini and Claude for this.
• Grounded (GR): what a model says when it searches live sources. Waikay lists Perplexity's Sonar and Gemini for this.

An error in a grounded answer usually comes from a page the model found, so fixing that page or source can change the answer within weeks. An error in a training-data answer reflects what the model learned before its cutoff, so it tends to persist until a newer model is released. Waikay also notes that its reports use neutral API responses, while your own logged-in chats may be shaped by your history and location, so the two can differ.

How do you correct AI hallucinations about your brand?

Waikay's own guidance lists the main routes: make your own website state the facts clearly, and contact the sources where wrong information originated. A practical workflow:

1. Collect the flagged facts and group them: wrong prices, outdated products, confused identities, wrong locations, invented features.
2. Find the likely source for each, using the sources report and a web search for the wrong claim. Old press releases, directory listings, review sites and similarly named companies are common culprits.
3. Fix your own pages first. State the correct fact plainly on the relevant page, with a date where it changed, such as "we discontinued the Basic plan in March 2026".
4. Correct third-party sources: update directory and marketplace listings, and ask publishers to correct or update articles.
5. Keep facts consistent across your site, profiles and listings, so models see one version.
6. Recheck after recrawl: grounded answers should improve within weeks; training-data answers may need a model update.

Waikay's help centre mentions llms.txt as an emerging way to guide AI models, while noting it is not supported yet. There is little evidence that AI search uses llms.txt today, so focus on the pages and sources above. A fuller method for aligning your facts everywhere is set out in how to make a brand's information consistent across AI-visible sources.

Waikay Lite pricing: what do you get for $29 a month?

Waikay does not list a plan called Lite or a plan at $29 a month. The $29 figure often seen in comparisons is the entry price of other tools, such as Otterly's Lite plan. Waikay's own pricing, as listed on its site and help centre:

Extra reports can be bought as packs: 40 for $99.95 or 80 for $189.95. Plan names differ slightly between Waikay's pages, and prompt limits are described differently on the entry plan, so confirm current allowances before buying. In practice, the $24.95 plan is closest to what people mean by a "lite" option: enough for a monthly fact review of one brand and a modest prompt set.

What are its strengths and limits?

Strengths

• Fact-level review that most trackers lack, with a simple accept or flag workflow.
• Training versus grounded labelling, which tells you which errors can be fixed quickly.
• Knowledge graph comparison between your site and AI answers, built on the entity work of its sister product, InLinks.
• Low entry price and a free tier.

Limits

• Credit-based reports, so frequent full reports use credits quickly.
• Engine coverage is narrower than some trackers, and differs between fact reports and prompt tracking.
• API-based fact reports may not match what logged-in users see.
• It finds errors but cannot fix them; corrections still depend on your pages and third-party sources.

How does it compare with other trackers?

Most AI visibility trackers focus on whether you are mentioned and cited. Waikay focuses on whether what is said is true. For broad daily tracking, cheaper or more engine-rich tools may suit better, as compared in Waikay vs Otterly vs Knowatoa. Profound's FactCheck and AthenaHQ's claim review offer similar accuracy checks at enterprise prices. Many teams pair a mention tracker with a monthly Waikay fact review.

Who is it for?

• Good fit: brands that have rebranded, changed pricing or products, merged, or share a name with another company, and small teams wanting an inexpensive accuracy check.
• Reasonable fit: agencies auditing clients' AI representation as part of onboarding.
• Poor fit: teams whose priority is daily share-of-voice tracking across many engines.

What might this look like in practice?

Consider an illustrative accounting software company, Ulvstead, that renamed its plans a year earlier. Waikay's facts showed ChatGPT and Claude, answering from training data, still describing the old "Solo" plan and a price that no longer existed. Perplexity's grounded answers repeated the same price, citing an old comparison article. The team added a dated note about the plan change to its pricing page, asked the article's publisher to update it, and corrected two directory listings. Within a month, Perplexity's answers used the new price; the training-data answers changed only after a later model update.

What do people ask about Waikay?

Does Waikay have a $29 Lite plan?

No. Its lowest paid plan is $24.95 a month; the $29 price in comparisons belongs to other tools.

How does Waikay detect hallucinations?

It extracts the facts AI models state about your brand and lets you mark each as accurate or flag it as wrong.

Can Waikay fix wrong AI answers?

No tool can directly. Waikay helps you find errors and their sources; you correct your pages and third-party listings.

Is there a free plan?

Yes, with 3 credits that do not renew, covering a brand overview, a topic report and some prompt tracking.