LLM Visibility Tracking Softwares - Are they worth the price?
Generative Engine Optimization is rising. People ask ChatGPT, Perplexity, Gemini, and Copilot before they click. If your brand shows up inside those answers, you get seen. If it does not, you fade.
Ranking in LLM answers now matters like blue links once did. LLM Visibility Trackers make it practical.
How Does A LLM visibility tool work
LLM Visibility Trackers, or AI Visibility Trackers, watch the answers that matter to you. They keep a log of citations that were mentioned against your prompts. They keep a timeline so you can tell if last week’s fix did anything. It is like an AI rank tracker, but for answers and sources, not positions.
In practice they:
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Run a small set of brand and category prompts
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Test across engines and locations on a schedule
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Save full answers, with time stamps
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Capture which URL gets the credit
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Flag risky claims and missing facts
Few LLM Visibility trackers like radarkit, profound and scrunch run real browsers with proxies, so a user in Berlin or Bengaluru would see the same thing. Nothing magic. Just careful, repeatable checks.
Which LLM Visibility trackers are the best
1. RadarKit.ai is best for reality checks. Real browsers and proxies, location runs, URL level citations with context, GA4 link for traffic checks. Entry price 29 per month with a 7 day trial. Desktop first. Data refreshes in minutes. Strong when you want what a user actually sees today. Radarkit’s data is unmatchable as they read all the LLMs in realtime unlike others who run API calls in the backend to give you response data. If accurate data is your thing then Radarkit is the best bet.
2. Profound - Best for clean dashboards and trend reading. Tracks mentions, sentiment, and citations across engines. Higher price. No focus on proxy based live runs, so a few regional quirks may slip by. Good when you need reporting for stakeholders.
3. Otterly AI - Best for enterprise clients. Share of voice and sentiment in one place, plus light GEO style audits that nudge fixes. Friendly entry price. Some features sit behind higher tiers. Solid pick when you want monitoring with a few practical steps. Otterly has a partnership with Semrush so the pricing can go high.
Data Inconsistency with LLM Trackers
Coverage differs by tool and budget. Browser runs with location control cost more and can be slower, but they match reality.
Freshness is a choice. Daily is enough for most SaaS and AI teams. Twice a week also works. More only if you ship fast or live in a regulated niche.
No tool helps if no one closes the loop. Someone has to read alerts, ship edits, and check again.
Policy and rights keep moving. Keep an audit trail of what you saw and when.
How to use a LLM Visibility Tracker
Keep it small and real.
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Pick ten prompts you must win. Five branded. Five category.
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Choose two engines and two cities.
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Track daily for two weeks. Export answers and citations.
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In week two, fix three pages that matter. Add a short fact box. Make specs scannable. Link one or two neutral proofs.
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Try to earn two fresh third party mentions that confirm those facts.
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Weeks three and four. Look for fewer errors, more citations, clearer snippets.
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If half your prompts improve, the price is fair. If not, refine prompts or try another tool.
Do not track only branded prompts. Add problem and category prompts where new users start.
Do not chase vanity counts. Care about citations and answer quality.
Do not hide facts inside long copy. Use short lines, numbers, dates, and clean tables.
Keep Crunchbase, Wikidata, and review sites in sync with your site. Consistent facts travel well.
Reviews
In our internal testing Radarkit and Profound were the clear winners. Radarkit for its robust infrastructure of fetching results in realtime and profound for its content and data capabilities. If I had to choose anyone between them I would buy both and use Radarkit for tracking and profound for citation data. Both are good in their own aspects.
Bottom line
If AI answers can move your sales or your reputation, LLM Visibility Trackers are worth it. Start small. Measure weekly. Publish facts that models can lift without guessing. Keep the loop tight. You will know it works when answers stop surprising you, and your link wears the little crown more days than not.
