Core concepts

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How they fit together

Connector (CSV / Postgres / MySQL / Snowflake / Azure SQL / Delta Sharing / Fivetran)
   └── Dataset (one per table, curated Parquet copy)
         │
         ├── Chat — Summand, grounded in the dataset
         │
         ├── View(s) — SQL transformations, optionally cross-dataset
         │      └── Chat or experiments can target a view instead of the dataset
         │
         └── Experiment(s) — scheduled component runs
                └── Components produce computed-context artifacts per run
                      └── Visible to chat, queryable downstream

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Identifiers

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Lifecycle

1. Create connector — upload a CSV or point Summand at a data source.
2. Datasets land automatically — for CSVs, one dataset on upload; for databases, one per table you enable.
3. Pick an action:
   • Chat with Summand for ad-hoc questions.
   • Create a view to save a SQL transformation you’ll re-use.
   • Create an experiment to schedule recurring analysis.
4. Read results — through chat or on the dataset’s Components tab.
5. Share — grant access to teammates by email or to a verified domain, or file the resource in a workspace.
6. Refresh — for live sources, click Refresh to re-read; experiments will re-run on their next schedule against the fresh data.