> For the complete documentation index, see [llms.txt](https://docs.kula.digital/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kula.digital/get-started/ask.md).

# What you can ask

You ask in plain English, the way you'd ask a sharp ops manager who knows your numbers. Below are starting points grouped by the problem you're trying to solve, with a note on what each one gives you back.

You don't have to phrase them exactly like this. Ask follow-ups. Push back. The AI is reading *your* data, so the more specific you get, the better the answer.

> **Tip:** the first time you ask a deep question, ask the AI to [run the diagnostic](/get-started/first-diagnostic.md) first. It connects your data together so the answers that follow are trustworthy.

## Keeping members (retention & at-risk)

* *"Who'd love a check-in from me this week?"*
* *"Which members haven't I seen in the last 30 days who used to come regularly?"*
* *"How is the reformer cohort tracking — are they staying with us?"*
* *"Show me members whose attendance has quietly dropped off in the last two months."*

**What you get back:** a named list (when your permission level allows names) of specific members, with how long since their last visit and what changed, so you can reach out today — not a generic "improve retention" tip.

## Your classes & schedule

* *"Which classes are quietly growing, and which could use a refresh?"*
* *"What are my busiest and emptiest time slots over the last quarter?"*
* *"Is the new instructor's class finding its people?"*
* *"Which classes should I think about cancelling based on attendance trends?"*

**What you get back:** attendance trends by class and time slot, with the direction of travel — so you can make schedule calls on evidence, not gut.

## Money & cash (revenue, plans, payments)

* *"How did revenue move over the last 12 months?"*
* *"How do memberships compare to class packs for us — which holds people longer?"*
* *"Are we going to be okay next month?"*
* *"Which members are on plans that are about to lapse?"*

**What you get back:** revenue and plan trends drawn from your own payments and accounting, reconciled — with the gaps flagged where the data can't fully answer, rather than a confident guess.

## Members & outreach

* *"Draft a warm note to the members I haven't seen since May."*
* *"Who are my most loyal members I should thank this month?"*
* *"Pull together a list of members due for a plan review."*

**What you get back:** a ready-to-send draft and the list it's based on. **It writes the message; you send it** — Kula Intelligence never emails or messages members on your behalf.

## Marketing & acquisition

* *"Where are my new members actually coming from?"*
* *"How is our website traffic trending, and is it turning into bookings?"*
* *"What did we spend to acquire members last quarter, and was it worth it?"*

**What you get back:** acquisition and traffic trends from your connected marketing sources. If a source isn't connected yet, the AI tells you what's missing rather than inventing a number.

## When the answer says "I'm not sure"

A good answer sometimes is *"I can't fully answer that yet."* If a data source isn't connected, or your history has a gap, the AI will say so and tell you what to connect or fix. That honesty is the point — see [Keeping your connectors healthy](/your-data-sources/operating.md) to close gaps.

## What's next

* [Your weekly rhythm](/get-started/your-rhythm.md) — turn these into a habit.
* [Build your own & share with your team](/skills/build-your-own.md) — save your best questions as a repeatable skill.


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.kula.digital/get-started/ask.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.