Action Plan
A ranked to-do list, rebuilt from your AI visibility data nightly.
Whaily runs 18 rule modules over your stored answers, sources and brand-audit results every night, then uses one AI step to turn the output into clear recommendations. Each one carries an impact band, an effort estimate, a confidence level and a priority, so you know what to do first.
ChatGPT
Claude
Gemini
Perplexity
DeepSeek
GrokThe problem
A dashboard tells you what is. It does not tell you what to do.
Visibility drops, a competitor gets cited more, a brand-audit check fails: any dashboard can show you that number. None of it tells you which of the possible fixes to try first, who should own it, or whether it worked once it shipped. That gap is where AI visibility programs stall.
How it works
From sign-up to signal in minutes.
18 rule modules run every night
Each module checks one thing against your stored data: a prompt where visibility slipped, a high-authority source you are absent from, a failed brand-audit check, a competitor gaining ground. Together they cover the full picture, not just one metric.
An AI step rewrites the finding in plain language
The rule modules decide what the recommendation is. A single AI step runs afterward to turn that decision into a sentence a person can act on without reading a report first. The scoring itself is not left to the AI.
You triage, assign and track
Accept a recommendation to turn it into an action item, snooze it for later, or dismiss it. Accepted items go onto the board with an owner and a due date. Recommended, Board and Investigations are three tabs of one Action plan section, so the work lives in one place. Once the related metric moves, it shows up on Recent wins.
What you get
Everything you need, in one place.
Eighteen rule modules, one nightly run
Every module runs against the same stored answers, sources and audit results, so recommendations are consistent night to night, not one-off guesses.
An AI step for wording, not scoring
The AI pass rewrites the output of a rule module into plain language. It runs after the scoring is finished, so the impact band and priority come from the rule module, not from the model.
Impact, effort, confidence, priority
Every recommendation names the entity it is about, an impact band, an effort estimate, a confidence level and a priority, plus the reasoning behind it.
Grouped by initiative
Related recommendations are grouped together, so you work through one initiative at a time instead of picking through twenty unrelated line items.
A board that fits your team
Work as a list or a Kanban board. Assign an owner, set a due date, leave a comment, and jump back to the recommendation and the data behind it at any time.
Recent wins
After an action item closes, Recent wins shows what changed afterward, so you can tell which recommendations were worth doing.
A ranked queue, not a wall of numbers.
Recommendations grouped by initiative, each with its impact band, effort estimate, confidence and priority. Accept one to turn it into an action item, or dismiss it with a reason.
Why it matters
A dashboard tells you what is. This tells you what to do first.
Every AI visibility tool in this category can show a chart moving the wrong way. Fewer of them say which of the possible fixes to try, in what order, and whether it is worth the effort. Without that layer, a team looks at the dashboard, agrees something should happen, and by the following week nothing has, because the dashboard never said what "something" was.
Whaily builds recommendations so they can be checked. Eighteen rule modules run over your stored answers, sources and brand-audit results every night. Each one is a fixed piece of logic, not a model guessing, so the same input produces the same recommendation. Only the last step, turning the finding into a sentence, goes through an AI pass. That split matters: you can trust the score because you can see the rule that produced it, and you get a readable recommendation because a model wrote the sentence.
Grouping by initiative keeps the list workable instead of turning into twenty stray tasks. The board carries the recommendation context wherever the task goes, so an assignee never has to ask why a task exists. And because Recent wins compares the metric before and after an action closes, you end a quarter with a record of what you actually shipped and what happened next, not just a list of things you meant to do.
One board for everything the team owns.
Action items as a list or a Kanban board, with an owner, a due date, comments and a link back to the recommendation that created them.
Questions
The short answers.
Where do the recommendations come from?+
Does the AI decide what to recommend?+
What does the impact band actually mean?+
Can I ignore recommendations I do not want?+
How do I know if an action actually helped?+
Can I assign action items and set due dates?+
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