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

# Quickstart

> Connect data, then chat with it, query it, or schedule analyses — in five minutes.

This guide walks through the fastest path to a result: sign in, connect data, and pick whichever of the three primary actions matches what you want to do.

<Steps>
  <Step title="Sign in">
    Go to [summand.com](https://summand.com) and create an account. Free tier is the default — no card required.

    `.edu` users can request an Education upgrade from [support@summand.com](mailto:support@summand.com) for full Pro features at no cost.
  </Step>

  <Step title="Connect data">
    Click **Upload** and drop in a CSV (up to 50 MB on Free, 1 GB on Pro, 4 GB on Enterprise), or, on Enterprise, click **Add connector** for PostgreSQL, MySQL, Snowflake, Azure SQL, Delta Sharing, or any Fivetran-supported source.

    Summand registers the data as a **dataset** under a new **connector** and runs an immediate baseline analysis — column stats, types, missingness, distributions. Files under 100 MB land within a minute.

    There's no setup wizard. No target column to pick. No schema review. Once the dataset shows *Ready*, you're done with setup.
  </Step>

  <Step title="Pick what to do next">
    On the dataset, choose one of three primary actions:

    <CardGroup cols={3}>
      <Card title="Chat" icon="message-bot">
        Open Summand and ask in English. *"What's interesting in this data?"* is a fine first question.
      </Card>

      <Card title="Create a view" icon="layer-group">
        Build a SQL transformation — visual or code — that you can re-use across chats and experiments.
      </Card>

      <Card title="Set up an experiment" icon="flask">
        Schedule a component (predictors, surprise finding, custom) to run on a cron. Outputs are versioned.
      </Card>
    </CardGroup>

    These aren't sequential — pick whichever fits the problem you brought to Summand. The order in the rest of this guide is: chat first (lowest friction), then views (when you need durable SQL), then experiments (when you need recurring analysis).
  </Step>

  <Step title="Chat with Summand">
    Open the chat panel from the sidebar. Pin the dataset (or a view) to the conversation, and ask anything:

    * *"How many rows are in this?"* — quick fact.
    * *"Which columns are most correlated with `revenue`?"* — exploratory.
    * *"Build me a view that joins this with the customer table."* — Summand drafts SQL and offers to save it as a view.

    Answers come grounded in the dataset's [semantic layer](/concepts/semantic-layer), so the numbers match what queries return.
  </Step>

  <Step title="(Optional) Create a view">
    For SQL transformations you'll re-use, go to **Views** in the sidebar. Either:

    * **Build it visually** — pick datasets, drag in joins and filters, watch live Athena preview update as you go.
    * **Hand-write SQL** — drop into the code editor; preview is live there too.
    * **Have Summand draft it** — ask in chat, accept the suggestion.

    Saved views work as inputs to experiments and as data sources for chat.
  </Step>

  <Step title="(Optional) Set up an experiment">
    Go to **Experiments** in the sidebar and click **+ New experiment**. Pick:

    * **A source** — a dataset or a view.
    * **One or more components** — predictors, surprise finding, column stats, semantic-layer pieces. Each component has typed inputs you fill in (e.g. *which column to predict* for the predictor component).
    * **A schedule** — daily 3 AM, every 6 hours, weekly Monday, or a custom cron.

    Save and the experiment runs on schedule. Each run produces a versioned output you can read in chat or wire into another view.
  </Step>
</Steps>

## What just happened

Behind the scenes, you created:

* A **connector** — the typed pointer to your data source.
* A **dataset** — one per table, with the curated Parquet copy and column-level stats.
* (Optionally) one or more **views** and **experiments** that build on the dataset.

The dataset's detail page has nine configuration tabs — Overview, Schedule, Schema, Components, Context, Access, Notifications, Run history, Advanced — for tuning behavior. Most users never need to touch them.

## Next steps

<CardGroup cols={2}>
  <Card title="Connect a database" icon="database" href="/guides/connect-database">
    PostgreSQL, MySQL, Snowflake, Azure SQL — Enterprise tier.
  </Card>

  <Card title="Create your first view" icon="layer-group" href="/guides/create-a-view">
    Visual builder, code editor, or Summand-assisted.
  </Card>

  <Card title="Set up an experiment" icon="flask" href="/guides/set-up-an-experiment">
    Components, inputs, cron, run history.
  </Card>

  <Card title="Share with teammates" icon="share-nodes" href="/guides/share-a-dataset">
    Per-user grants and visibility settings.
  </Card>
</CardGroup>