Am I on AI Review 2025: Is It Worth the Investment?

Am I On AI is a visibility tracking platform built to show where — and how often — your brand appears inside AI-generated answers. It scans engines like ChatGPT, Gemini, Perplexity, and Copilot to find mentions of your company, products, or competitors, then compiles those results into dashboards that show your current footprint across multiple AI systems. Each mention comes with the full response text, the source or citation that fed the AI, and sentiment context, so you can see not only if you were referenced, but also what the AI said and where it drew the information from.

Behind the surface, the tool turns those findings into structured insights you can act on. You can track visibility trends week over week, compare your coverage to competitors, and identify which sources the models rely on when answering in your niche. Am I On AI also groups results by prompt category, language, and engine to help you understand which types of questions you already dominate and where you’re absent. The result is a single workspace where marketers, founders, or agencies can measure AI presence just as easily as they once measured SERP rankings — without needing to run manual tests or guess where their brand stands in generative results.

Despite its strong visibility tracking and clear reporting, Am I On AI has limitations like limited real-time coverage, occasional gaps across smaller AI engines, and minimal customization for power users who need deeper filtering or exports. Some users also note that scans can take time to refresh, and that not every mention captured is equally relevant or valuable. In this article, we’ll cover some of Am I On AI’s core features, how its pricing compares to similar GEO tools, and where its strengths — and blind spots — show up most clearly once you start using it.

Am I On AI pros: Three key features users seem to love 

Am I On AI packs several capabilities into a single dashboard, but a few stand out as the ones users talk about most. These are the features that make it feel more like a live visibility monitor than a static report—the ones that turn scattered AI mentions into clear, trackable insights you can actually use.

Prompt Tracking & Auto-Generated Queries

Am I On AI begins by translating buyer questions into specific, testable prompts, using auto-generated long-tail variants that reflect your products, competitors, and use cases. Those prompts run across supported AI engines in structured batches, after which the platform captures the full answers and classifies each result as a recommendation, a neutral mention, or a miss. You can extend this library with prompts pulled from sales objections, integration requests, or regional phrasing, which aligns scans with real conversations rather than hypothetical keywords. As results accumulate, the system organizes prompts into thematic clusters—alternatives, pricing, comparisons, implementation—so you can see which narratives consistently surface your brand and which remain invisible. Because every run is time-stamped and pinned to its prompt set, trend lines become meaningful: you can tie a new case study or doc update to a measurable change in inclusion for the entire cluster. Filters for engine, locale, and prompt theme narrow reviews to the contexts that matter, while side-by-side competitor visibility reveals whether an absence reflects model preference or a content gap you can fix.

Source / Citation Analysis

After answers are captured, Am I On AI follows the trail to the evidence that shaped them, extracting the pages, domains, and profiles that function as citations. The dashboard links directly to those sources with the relevant answer attached, which lets you validate whether the model reflected the source accurately or lifted a stale claim that needs correction. Over successive scans, the platform tallies recurring “influencer” domains within your category, building a ranked map of the places that most often steer recommendations. Slicing this view by engine exposes behavioral differences—for example, one model leaning on community threads while another privileges vendor documentation—which informs where you invest the next marginal hour. Because citations sit next to prompts and outcomes, you can test whether strengthening a single reference page improves inclusion across related questions, turning abstract model behavior into a practical content roadmap.

Recommendations / Action Items

With evidence in hand, Am I On AI converts findings into a prioritized queue of tasks, each tied to a specific failure mode observed in the scans. On-site guidance focuses on clarifying comparison pages, tightening feature explanations, and structuring FAQs so answers can be extracted cleanly, while off-site guidance targets the influential domains already shaping responses in your niche. Every task links back to the originating prompt, engine, and citation, which keeps owners accountable and makes validation straightforward during the next run. As you complete work and rerun scans, the queue reshuffles according to fresh results, establishing a closed loop where improvements rise or fall based on measured inclusion rather than opinion. Over time this loop turns sporadic fixes into an operating cadence, with the highest-leverage opportunities consistently moving to the top because the data proves they matter.

Am I On AI cons: Three key limitations users seem to hate 

Before diving into where the tool struggles, it helps to note that most of these limitations don’t come from bugs — they come from how Am I On AI is built and what kind of data it tracks. Because it’s pulling from live AI models and running structured scans rather than listening in real time, there are trade-offs between precision, speed, and depth. Understanding these gaps gives you a clearer sense of when the tool works best and when you may need to pair it with other systems.

Snapshot mode, not real-time monitoring

Am I On AI checks things in set scans, so your view updates after a run finishes, not the instant a change happens. That delay means a new press hit, a quick page fix, or a sudden rumor may sit unseen until the next pass rolls through. Because launches and crises often move in hours, that gap can hide short spikes that matter to sales, support, or PR. The tool still shines for weekly trends because batches smooth noise and show direction, yet this same batching blunts fast detection when speed is the goal. Teams that must react quickly usually pair it with a second alerting tool, then use Am I On AI for proof over time. In practice, the combo gives you quick pings for action and slower, steadier scans for learning.

Basic analytical depth and limited power features

The default views answer simple questions quickly, but mature teams ask for deeper cuts that the app does not always deliver. They want to slice results by product line, region, and engine in one pass, then stack those slices into custom charts that travel to a deck or a BI tool. They also want rules that trigger alerts, bulk exports that land on a schedule, and query joins that link prompts, sources, and revenue. When those needs arrive, the built-in filters feel tight, and work shifts to spreadsheets or scripts that live outside the app. That extra shuffle slows reviews, weakens source-of-truth habits, and adds risk when owners change. Power users solve this by keeping Am I On AI for discovery, then piping essentials to a warehouse where they can model, join, and share without friction.

Dependence on evolving AI models (black box risk)

Every score inside the tool comes from how outside models write answers, and those models can change without any warning at all. A quiet update can change which pages get cited or which brands get named, and your chart can move even though your site never changed. That drift makes planning tricky because last month’s win may fade after a model shift that no one controls. The logs help because they pin each result to a date, an engine, and a prompt, which lets you spot when behavior changed rather than when content failed. Even so, the ground keeps moving, so smart teams spread bets across owned docs, trusted third-party pages, and fresh comparison content that engines like to quote. Treat results as signals to guide the next test, then confirm the gain across several scans before you claim a stable win.

Am I On AI Pricing: Is it really worth it?

Am I On AI keeps its pricing simple with two core plans and an agency add-on for teams that manage multiple clients. The entry-level Single plan costs $100 per month and includes tracking for one product, up to 100 prompts, weekly scans, and personalized visibility recommendations. It also unlocks unlimited team seats and supports international monitoring — useful for companies that want to see how AI models describe them in different regions or languages. This plan gives smaller brands a manageable starting point, and because it includes weekly updates and recommendations, it’s built more like a continuous learning loop than a one-off audit. The low barrier to entry and the 14-day free trial (with no credit card required) make it easy to test whether the insights justify the spend before scaling up.

For growing teams or agencies managing several products, the Multiple plan raises the ceiling to $250 per month, covering up to three products and 300 prompts. It mirrors the same features as the Single plan but adds scale for brands with multiple verticals or product lines. Agencies get their own dedicated package starting at the same $250 tier, designed for three clients with extras like white-label dashboards and API access. That structure makes Am I On AI’s pricing predictable and transparent, and the ability to upgrade or downgrade mid-cycle (with prorated billing) keeps it flexible. The downside, though, is that weekly scanning may feel slow for teams used to daily tracking, and there’s no true enterprise customization listed beyond those tiers. For fast-moving campaigns or clients who want real-time visibility, the cost-to-refresh ratio might feel steep, but for most marketers and small agencies, the platform’s breadth of data and guided recommendations make the monthly fee a fair trade-off.

Analyze: The best and most comprehensive alternative to Am I on AI for ai search visibility tracking

Most GEO tools tell you whether your brand appeared in a ChatGPT response. Then they stop. You get a visibility score, maybe a sentiment score, but no connection to what happened next. Did anyone click? Did they convert? Was it worth the effort? 

These tools treat a brand mention in Perplexity the same as a citation in Claude, ignoring that one might drive qualified traffic while the other sends nothing.

Analyze connects AI visibility to actual business outcomes. The platform tracks which answer engines send sessions to your site (Discover), which pages those visitors land on, what actions they take, and how much revenue they influence (Monitor). You see prompt-level performance across ChatGPT, Perplexity, Claude, Copilot, and Gemini, but unlike visibility-only tools, you also see conversion rates, assisted revenue, and ROI by referrer. 

Analyze helps you act on these insights to improve your AI traffic (Improve), all while keeping an eye on the entire market, tracking how your brand sentiment and positioning fluctuates over time (Govern).

Your team then stops guessing whether AI visibility matters and starts proving which engines deserve investment and which prompts drive pipeline.

Key Analyze features

• See actual AI referral traffic by engine and track trends that reveal where visibility grows and where it stalls.

• See the pages that receive that traffic with the originating model, the landing path, and the conversions those visits drive.

• Track prompt-level visibility and sentiment across major LLMs to understand how models talk about your brand and competitors.

• Audit model citations and sources to identify which domains shape answers and where your own coverage must improve.

• Surface opportunities and competitive gaps that prioritize actions by potential impact, not vanity metrics.

Here are in more details how Analyze works:

See actual traffic from AI engines, not just mentions

Analyze attributes every session from answer engines to its specific source—Perplexity, Claude, ChatGPT, Copilot, or Gemini. You see session volume by engine, trends over six months, and what percentage of your total traffic comes from AI referrers. When ChatGPT sends 248 sessions but Perplexity sends 142, you know exactly where to focus optimization work.

Know which pages convert AI traffic and optimize where revenue moves

Most tools stop at "your brand was mentioned." Analyze shows you the complete journey from AI answer to landing page to conversion, so you optimize pages that drive revenue instead of chasing visibility that goes nowhere.

The platform shows which landing pages receive AI referrals, which engine sent each session, and what conversion events those visits trigger. 

For instance, when your product comparison page gets 50 sessions from Perplexity and converts 12% to trials, while an old blog post gets 40 sessions from ChatGPT with zero conversions, you know exactly what to strengthen and what to deprioritize.

Track the exact prompts buyers use and see where you're winning or losing

Analyze monitors specific prompts across all major LLMs—"best Salesforce alternatives for medium businesses," "top customer service software for mid-sized companies in 2025," "marketing automation tools for e-commerce sites."

For each prompt, you see your brand's visibility percentage, position relative to competitors, and sentiment score.

You can also see which competitors appear alongside you, how your position changes daily, and whether sentiment is improving or declining.

Don’t know which prompts to track? No worries. Analyze has a prompt suggestion feature that suggests the actual bottom of the funnel prompts you should keep your eyes on.

Audit which sources models trust and build authority where it matters

Analyze reveals exactly which domains and URLs models cite when answering questions in your category. 

You can see, for instance, that Creatio gets mentioned because Salesforce.com's comparison pages rank consistently, or that IssueTrack appears because three specific review sites cite them repeatedly. 

Analyze shows usage count per source, which models reference each domain, and when those citations first appeared.

Citation visibility matters because it shows you where to invest. Instead of generic link building, you target the specific sources that shape AI answers in your category. You strengthen relationships with domains that models already trust, create content that fills gaps in their coverage, and track whether your citation frequency increases after each initiative.

Prioritize opportunities and close competitive gaps

Analyze surfaces opportunities based on omissions, weak coverage, rising prompts, and unfavorable sentiment, then pairs each with recommended actions that reflect likely impact and required effort. 

For instance, you can run a weekly triage that selects a small set of moves—reinforce a page that nearly wins an important prompt, publish a focused explainer to address a negative narrative, or execute a targeted citation plan for a stubborn head term.