AI visibility platform
Know when AI recommends you, cites you, or skips you.
Cheers gives multi-location service brands a practical AI visibility layer: recommendation tracking, cited-source analysis, competitor comparisons, profile and review diagnosis, and frontline actions that improve the evidence AI systems rely on.
For CMOs, growth leaders, reputation teams, and operators who need AI-search visibility to become a measurable channel, not a screenshot in a slide deck.
Operating view
What Cheers shows the team.
Cheers brings field activity, reviews, website gaps, and AI answers into one view, so marketing and operations can decide what to fix by location.
Prompts
Track buying-intent questions by service category, market, provider, and location.
Citations
Identify review sites, directories, owned pages, competitors, and sources AI engines use.
Local work
Tie action plans to reviews, profiles, citations, local pages, and frontline behavior.
Product path
From the tap to the recommendation.
NFC badges and QR flows capture the customer moment. The rest of Cheers shows whether it produced a review, strengthened the location, and changed how the market sees the business.
NFC + QR capture
Badges, QR flows, and review links give field and front-desk teams a simple way to ask after a good customer moment.
Employee attribution
Taps, link clicks, reviews, ratings, and conversion rates roll up by employee, location, region, and brand.
Location intelligence
Google profiles, review velocity, citations, competitors, and location pages are monitored by market.
AI visibility
Tracked prompts show when ChatGPT, Gemini, Perplexity, and Google recommend you, cite you, or skip you.
The gap
AI search is opaque unless you measure the answer and the sources behind it.
A customer can now ask ChatGPT, Gemini, Perplexity, or Google who to hire and get a short list before visiting your website. Cheers shows where your brand appears, which competitors appear instead, and which reviews, citations, profiles, and pages shape the recommendation.
See whether AI assistants recommend your locations for high-intent local searches.
Understand which competitors own the answer in each priority market.
Map cited sources back to review sites, directories, local pages, and business profiles.
Move from AI visibility reporting to specific fixes for reviews, profiles, citations, and local pages.
High-trust moments
Where Cheers fits the work already happening.
The strongest review request is not a blast from corporate. It is the ask that happens after a real service moment, with attribution back to the team that created the trust.
Prompt setup
Track real buyer questions by service category, market, urgency, and comparison intent.
Answer capture
Record whether the brand appeared, was recommended, was cited, or was displaced.
Source diagnosis
Classify cited sources as business, review platform, directory, competitor, asset, or AI source.
Local fix
Connect visibility gaps to reviews, profiles, citations, local pages, and employee-led review capture.
Recommendation tracking
Measure what buyers actually ask.
Cheers tests local, category-specific prompts across AI assistants so teams can see recommendation share by market rather than guessing from generic brand queries.
Prompt sets for service category, city, urgency, and buying intent.
Market-level competitor comparisons.
Tracking across ChatGPT, Gemini, Perplexity, and Google search experiences.
Citation intelligence
Find the sources behind the answer.
AI recommendations are shaped by public evidence. Cheers identifies the review sites, directories, local pages, articles, profiles, and competitors that appear to influence the answer.
Cited-source analysis by market and prompt.
Gap reporting for directories, citations, and location pages.
Source patterns tied back to practical SEO and reputation work.
Operating system
Improve AI visibility by fixing local evidence.
Reporting alone does not create recommendations. Cheers connects AI visibility gaps to frontline review capture, profile quality, citation cleanup, and employee-level accountability.
Review velocity and attribution workflows.
Profile and citation diagnostics.
Prioritized next steps for locations with weak recommendation share.
Where to start
Start with the locations where the evidence is weakest.
Define priority categories, markets, competitors, and buying-intent prompts.
Measure AI recommendation share and cited sources by location.
Diagnose the reviews, profiles, citations, and pages behind each gap.
Activate frontline review generation where evidence is weakest.
Monitor recommendation share as reviews, profiles, citations, and pages improve.
FAQ
What does an AI visibility platform measure?
An AI visibility platform measures whether AI assistants mention or recommend a brand for target prompts, which competitors appear, what sources are cited, and which public evidence may influence the answer.
Can AI visibility be improved without fake content?
Yes. Cheers focuses on legitimate local evidence: better review velocity, accurate profile data, stronger citations, clearer local pages, and frontline accountability for the customer experiences that create reputation.