The hard part is reading the data
Setting a tool up is rarely the problem. Making sense of what it shows you is.
We read a sample of 581 G2 reviews of ten AI visibility tools. About one in three reviewers who named a real complaint said the same thing: the tool was hard to learn, or took work to set up. Learning came up about four times as often as setup.
Yet about half of the reviewers praised their tool as easy to use. So “easy to use” on a vendor’s page tells you little. The better question is what the tool leaves on your desk: choosing the prompts, reading hundreds of answers, deciding what to change.
The seven questions below are built to find that out.
The sample: 581 reviews of ten tools on G2, read in October 2026 and sorted by the kind of praise and complaint in each. It is a sample, not every review.
Seven questions to ask any tool
Each question asks the same thing from a different side: what does the tool do, and what does it leave for you? Put them to every vendor on your list, us included.
How are the prompts written?
The answers you see depend on the prompts: the questions put to the engines. Most tools ask you to choose them, and suggest more. A few build the set for you, from your market or from who your buyers are. Even if you know what you want to track, a good prompt set should show you questions you had not thought of.
“What are the prompts based on, and how do you know they match how my buyers ask?”
Which AI engines do you need?
A long engine list looks good on a pricing page. What matters is which engines your buyers ask. For most markets that is a short list. Check that those are on the plan you’d buy, not only on the top one.
“Which of the engines my buyers use are on the plan I’m looking at?”
Can you trust a change when you see one?
Ask an AI engine the same question twice and you can get two different answers, so a single reading proves little. Look for a tool that asks the same prompts again on a schedule, keeps the history, and bases each reading on enough answers to mean something.
“When my number moves, how do I know the change is real?”
How are you described, and next to whom?
Being named is not the same as being recommended. Many tools give you a sentiment score and leave the reading to you. Look for one that tells a recommendation apart from a cautious mention or a warning, and shows which competitors appear beside you, prompt by prompt.
“Can you show me where an engine was cautious about us, and who it recommended instead?”
Can you see why each answer reads as it does?
Most tools list the sites an engine cited, and leave you to work out what mattered. A few go further and show what the engine weighed, such as price, fit or trust. Ask to see one answer with its sources and its reasons.
“Can I open one answer and see why it said what it said?”
Does it tell you what to change, and in what order?
Many tools now give you a ranked list of actions. Ask what the ranking is based on, and whether it shows what has to be done first. Ask, too, whether it checks that a change you made is live.
“Why is the top action on top?”
Who writes the content?
Some tools draft and publish for you. Others hand you a brief and leave the writing to your team. Decide whether you want a writer or a map.
“What do I get: a finished page, or a brief?”
Where Talebeacon fits
We do the thinking nobody has the hours for. You keep the judgement.
Nobody has time to read hundreds of AI answers. We do. That is true for a team with a GEO specialist, and for a team without one.
We work out which questions matter. We start from the page you give us: what you sell, who buys it, and who you compete with. From that we model the kinds of buyer in your market. Then we follow each one from noticing a problem, to searching, to comparing, to choosing. At every stage we ask what this person would type into an AI engine that could lead them to you, or to a competitor. The result is a map of the questions that matter for you, hundreds of them. You approve the buyer types before anything runs. You never have to guess which prompts are worth tracking.
We read every answer. Every engine answers every question. We sort each answer by how it treats you: recommended, mentioned, mentioned cautiously, warned, or left out. Then we trace it back to what the engine read and what it weighed, such as price or fit. You see the pattern, with the evidence open to check.
We turn it into a plan. You get a list of what to change, most useful first. Each step says why it comes first, who on your team should do it, what to make and where to put it. Where we can, we check your site and tell you when the change is live. Bigger steps, like a new page, come with a quick version and a full one, a first task you can finish in 15 minutes, and an AI prompt to draft the writing. After every scan we rebuild the plan.
What stays with you. Your team makes the changes, and decides which ones are worth making. If what you want is to pick your own prompts and watch them every day, a prompt tracker is the better tool.
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Four short questions
What is an AI visibility tool?
Software that asks AI engines like ChatGPT, Claude and Gemini the questions your buyers ask, and records whether you appear in the answers, how you’re described, and who appears beside you. Some tools stop there and leave the reading to you. Others read the answers for you and turn them into a list of work to do.
Do I need a GEO specialist to use one?
Not with every tool. Some expect someone on your team to choose the prompts and make sense of the data. Others do that reading for you. A specialist is still worth having: the tool does the groundwork, and they decide what to act on.
Do I need daily tracking?
Probably not. What you need is enough answers behind each number. AI engines give different answers to the same question, even on the same model with the same settings, so one reading proves little. A tool can get there by checking every day, or by checking a few times a week. Weekly is about the floor. Less often than that, and you cannot tell a real change from noise.
How long before changes show up in AI answers?
It depends on how the engine gets its answer. When an engine searches the web and reads your page live, an edit can show up the next time someone asks. A new page has to be found and indexed first, which can take days or weeks. What a model learned in training changes far more slowly, often over months. Quick changes can also fade. A lasting shift in how engines describe you takes longer to build. Asking the same prompts again over time shows what has held.
More on the basics: What is Generative Engine Optimization (GEO)? · What to fix first: a marketer’s guide to AI visibility