> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kepler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Example workflows

> See the Kepler connector end to end in real conversations.

These examples show how a conversation flows once the connector is set up. You ask in natural language; your assistant calls the right Kepler [tools](/mcp/tools) and brings back cited results. The tool names below are shown only to make the mechanics clear. You never type them.

## Ask a research question

<Steps>
  <Step title="You ask">
    > Use Kepler to explain what drove Costco's gross margin over the last three fiscal years.
  </Step>

  <Step title="Kepler starts a run">
    Your assistant calls `run_financial_research` and gets back a `conversation_id`. It then calls `get_run_result` to wait, streaming progress as Kepler reads the 10-K filings.
  </Step>

  <Step title="You get a cited answer">
    A few minutes later the answer arrives, with each margin figure linked to the filing it came from and a sourcing report on coverage.
  </Step>

  <Step title="You follow up">
    > Now compare that to Walmart for the same period.

    Your assistant calls `continue_research` on the same conversation, and Kepler keeps full context and extends the analysis.
  </Step>
</Steps>

## Build a model and export it

<Steps>
  <Step title="You ask">
    > Use Kepler to build a 3-year annual income statement for Apple, each line cited to its 10-K.
  </Step>

  <Step title="Kepler returns a workbook">
    The result includes a workbook with a download link and an in-app link. Every cell traces back to a source.

    <Frame caption="A finished workbook rendered in the conversation, with the sources Kepler used listed alongside.">
      <img src="https://mintcdn.com/kepler-a6ccf6fc/RVsTkAHzK349hoa0/images/workbook-result.png?fit=max&auto=format&n=RVsTkAHzK349hoa0&q=85&s=a2c60fbaef55648a9a567b20142ef287" alt="Apple income statement workbook built by Kepler, with sources and the answer summary" width="2800" height="2100" data-path="images/workbook-result.png" />
    </Frame>
  </Step>

  <Step title="You pull the data">
    > Give me the income statement sheet as CSV.

    Your assistant calls `get_workbook_data` and returns the full sheet as CSV for use in your own tools.
  </Step>
</Steps>

## Check coverage before a deep dive

Availability questions don't need a full run. They return instantly through `lookup_company`.

> Use Kepler to check whether Nvidia's latest 10-K and Q3 earnings call are available.

Your assistant lists the filings and transcripts Kepler has on file, with forms, dates, and fiscal periods, a quick way to confirm coverage before asking for analysis.

## Pick up an earlier run

> Find the Netflix model I built yesterday and add a fiscal 2025 column.

Your assistant calls `list_recent_runs` to locate the conversation, then `continue_research` to extend it, with no need to start over.

## Tips for better results

<AccordionGroup>
  <Accordion title="Name the company and the periods">
    "Apple's last three fiscal years" beats "Apple recently." Specific periods give Kepler a clear target and make citations easier to verify.
  </Accordion>

  <Accordion title="Say what structure you want">
    For models, describe the statement type, segments, and formatting up front, since it's faster than reshaping afterward.
  </Accordion>

  <Accordion title="Expect runs to take a few minutes">
    Kepler reads primary sources rather than summarizing from memory. The wait is the work; your assistant streams progress while it goes.
  </Accordion>

  <Accordion title="Keep the thread going">
    Follow-ups stay in the same conversation, so Kepler remembers prior results. Start a new request only when you switch topics.
  </Accordion>
</AccordionGroup>