Otterly.ai vs running the prompt panel by hand
the method is identical, the discipline is not
By Jānis Plūme, Founder, Outbound Pros · 8 min read · 2026-08-06
Quick answer
Otterly.ai and a spreadsheet run the same method. Both ask a fixed set of prompts against the assistants on a schedule and record whether you were mentioned, whether you were linked, which competitors appeared and how you were described. The tool removes the repetition, keeps the history you would otherwise forget to keep, and samples at a cadence a person will not sustain. The manual version costs an afternoon a month and teaches you far more, because you read the raw answers rather than a chart derived from them. Neither designs your prompt set, and prompt set design is the thing that decides whether any of the resulting data means anything.
Why the method is the same in both columns
No first party citation reporting exists for ChatGPT app answers, Claude or Perplexity, from anyone. So for those surfaces there is exactly one honest instrument available to anybody outside the platforms: ask the question the way a buyer would ask it, in a clean session, and write down what came back. That is sampling. Every paid product in this category is doing it, and a person with a spreadsheet is doing it too. The difference between the two columns is operational rather than epistemic, and it is worth being clear about that before comparing anything else.
Otterly.ai is a small Austrian product with unusual scope discipline. It monitors a prompt set across the assistants and the Google AI surfaces and reports three things: whether your brand was mentioned, whether your links were included, and how the answer characterised you. There is no crawler analytics module, no modelled prompt volume, no composite index rolling five engines into one number. In a category where the commercial temptation is to add a surface every quarter, shipping something a small team can finish reading is a defensible position and an unusual one.
Reporting mentions and links separately is a small feature with a large implication, and it is the strongest argument for the tool over the spreadsheet. Being mentioned and being linked are different outcomes with different downstream value. A brand mentioned without a link still shapes what the buyer believes, which matters, and it sends nobody to your site, which also matters. Most manual panels collapse the two because the person recording them is in a hurry.
| Dimension | Otterly.ai | A manual panel in a spreadsheet |
|---|---|---|
| Method | Scheduled prompt runs recorded and stored. Sampling, presented without a composite score dressing it up as a measurement. | Identical sampling, performed by a person. The epistemic status of the data is the same in both columns. |
| Who designs the prompt set | You do. No tool enforces a good one, and a flattering set produces a flattering chart because the product faithfully reports what was asked. | You do. The advantage is that writing them by hand makes the self flattery obvious while you are doing it. |
| Cadence and noise | Regular and automatic. Assistant answers are unstable and the same prompt can return different sources on consecutive runs, so a consistent cadence is what makes a series readable. | As regular as your calendar, which in practice means it happens twice and then a launch eats the third month. |
| History | Kept for you, from the day you start, without anybody remembering to keep it. | Kept only if somebody is disciplined about the same columns in the same file. This is where most manual programmes actually die. |
| Session hygiene | Handled by the product, consistently, which removes a common source of contaminated results. | Yours to enforce. Fresh session per prompt, no chat history, no memory or personalisation active, or you are measuring your own account rather than the model. |
| Mentions against links | Reported separately, which is more useful than it sounds and more than most trackers bother with. | Possible, and usually skipped by whoever is doing the recording at half past five. |
| What you learn | That the trend moved, and roughly where. The reading is fast and the understanding is second hand. | Considerably more, because you read the actual answers. Nothing teaches you what your category sounds like to a model faster than reading thirty of its answers in one sitting. |
| Ceiling | Arrives when you need per market or per product line views, deeper cited source detail, or several people needing different slices. That is a healthy way for a starter product to fail. | Arrives sooner, and it arrives as fatigue rather than as a missing feature. |
Where Otterly.ai wins
It wins on persistence, which is the failure mode of every manual measurement programme ever attempted by a busy team. The panel you run in month one is excellent. The panel you did not run in month four is worthless, and the gap is invisible until you need a trend and discover you have three data points spread over a year. Automating a method you already believe in is a legitimate purchase, and this is the clearest example of it in the category.
- A baseline that exists whether or not anybody remembered, which is most of the value and almost none of the marketing
- Consistent session hygiene, removing the most common way a hand run panel quietly contaminates itself
- Mentions and links reported separately, which sharpens what you do next
- How the answer characterised you, which is the field that matters more than the mention count and the one manual runs record least reliably
- A very low floor. Set up in an afternoon and readable by anyone, which for a team of one is the deciding property rather than a nice detail
Where running it yourself wins
It wins on understanding, and understanding is what you are short of at the start rather than data. Reading thirty raw assistant answers about your category in one sitting is the fastest education available in this field. You see which competitors are treated as the default, which third party pages keep getting cited, which of your claims the model has absorbed and which it has never encountered, and how your company is described when nobody is trying to be kind. None of that survives compression into a visibility percentage.
- It tells you what you actually want a tool to automate, so the eventual purchase is a decision rather than a hope
- It surfaces the cited third party sources directly, and that list is a work plan whether or not you ever buy anything
- It costs an afternoon and no procurement, which means it can happen this week rather than next quarter
- It exposes bad prompt design immediately. Writing a prompt you know your buyer would never type is uncomfortable in a way that selecting it from a dropdown is not
- It works for the surfaces and the questions no product has got to yet, because you are not limited to what a roadmap has shipped
The discipline that makes either version worth anything is the same: a frozen prompt set spanning definitional questions in your category, comparison and selection questions, and questions where your own original data would be the correct answer. Twenty to thirty is enough for a single B2B product. If the set drifts every month, you are measuring your own editing rather than the models.
Who should pick which
- Run it manually first, once, regardless of what you intend to buy. One afternoon, thirty prompts, four columns: were you cited, was a competitor cited, which URL was cited, was the description accurate
- Buy Otterly.ai when the repetition is the obstacle rather than the curiosity, which is usually month two or three, and when you know from experience which fields you care about
- Buy it immediately if your worry is that assistants describe your company inaccurately. That is a yes or no question with an urgent fix attached, and paying for a consistent watch on it is cheaper than the delay
- Stay manual if AI search is still an open question in your category and you are deciding whether it deserves any budget at all. A spreadsheet answers that without a subscription and without a sunk cost pulling the conclusion
- Move past both when you need per market or per product line views, deeper cited source detail, or reporting several people can read differently. Plan that migration rather than being surprised by it
And the same caveat that belongs on every page of this site belongs here. Both columns are blind to your own infrastructure. If your pages return an empty root element to a non rendering crawler, both will show you a low visibility number and no cause, and you will spend a quarter writing content nothing ever read. That check is free and takes ten minutes, and it should happen before either column does.
Disclosure: we are not a neutral party
We sell managed outbound through the Outbound Pros group, so a company that builds a working inbound channel is a company that stops needing us. If you want to see what we actually take money for rather than guess at it, the group sells managed LinkedIn outreach and cold email alongside it. There is no affiliate arrangement with Otterly.ai or with anything else named on this site, so the only bias present is structural and it argues against the entire inbound category. Which is worth holding while you read the recommendation above, because it tells most readers to do the free version first and then buy the smallest product in a market we compete with. The longer write up sits at our Otterly.ai review.
Questions we get asked about prompt panels
How many prompts should I track?
Twenty to thirty is enough for a single B2B product, and the count matters far less than the freezing. A stable set run identically every month produces a trend you can read. A set you edit each month produces a chart of your own editorial decisions. Spread them across definitional questions in your category, comparison and selection questions a buyer would actually type, and questions where your own original data would be the right answer.
Does running it by hand actually work?
Yes, and it is the method we recommend everybody run at least once. Open a fresh session per prompt with no chat history and no memory or personalisation active, ask each assistant the same question, and record four fields. The failure is never the method, it is the calendar. If you have run it three months in a row without being nagged, you probably do not need to automate it. If you have not, that is your answer about the subscription.
Will a tool make my numbers less noisy?
It will make them more comparable, which is not the same thing. Assistant answers are genuinely unstable and the same prompt can return different sources on consecutive runs, so the noise is in the phenomenon rather than in your instrument. What consistent cadence and consistent session handling buy you is the confidence that a movement is in the models rather than in how you happened to ask this month. Read the series quarterly for judgement and monthly at most for direction.
When have I outgrown a light tracker?
When you need the answer segmented by region, product line or buyer segment; when you want cited source detail deep enough to build an off domain placement programme from; or when several people need different slices of the same data and are asking you to export things. Those are growth problems and they arrive later than most buyers expect. Until then, the ceiling is theoretical and the floor is what decides whether the programme survives.
Ungated and nothing is stored. If a non rendering crawler receives an empty container from your pages, no prompt panel in either column will ever tell you why.
Last updated: 2026-08-06
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