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Underneath every live view is a table — rows and typed columns, like a spreadsheet your assistant maintains for you. The data layer is where all of those tables live.

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How data is organized

Tables are arranged like folders. A trucking operation might have Data/Samsara/daily_snapshots; a housing team might keep Data/Housing/arrears and Data/Housing/repairs; an online shop might have Data/ExternalAPI/Orders. Your assistant names and organizes tables sensibly as it works — and you can always ask it to restructure. Each table has a defined shape: columns with real types (text, numbers, dates, true/false, lists), optional unique keys so duplicates can’t sneak in, and a description of where the data came from.

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Where data comes from

Anything your assistant can reach can become a table:
  • APIs and external systems — pulled once, or repeatedly on a schedule: each run appends fresh rows, and a history builds up over time. This is how you get time series — a table of API response times, daily order counts, truck locations at 9am.
  • Files you share — spreadsheets, CSVs, exports. Send them over any channel and your assistant can extract the contents into proper tables.
  • Connected apps — records from your integrations (CRM contacts, tickets, invoices) ingested for analysis.
  • Its own computations — intermediate results worth keeping: a cleaned dataset, a monthly aggregation, a scored list. Instead of redoing the work each time, the result is stored and reused.

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What your assistant does with it

Once data is in tables, you can ask for anything you’d ask an analyst:
  • Filter — “show me the high-priority repairs.”
  • Aggregate — “total spend by region”, “average response time per day”, counts, sums, averages, medians and more, grouped however you like.
  • Combine — “join the arrears table with the payments table and show me outstanding balances.” Tables from completely different sources can be combined into one answer.
  • Derive — “add a total column: unit price times quantity.” New columns computed from existing ones.
  • Search by meaning — for text-heavy tables, “find the complaints about billing” works even when no row contains the word “billing”.
The answers come back in chat — or become permanent dashboard tiles if they’re worth watching over time.

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The Data pane

The Data pane — in the Brain section of your assistant’s page — is your window into the whole data layer. The Console describes it as “everything your teammate has ingested — browse nested tables like a directory and open any to view rows”, and that’s exactly the job: it’s where you verify, audit, and explore the raw material behind your assistant’s answers and dashboards. What it’s for:
  • Checking what was collected. After a scheduled task pulls from an API, open its table and confirm today’s rows actually landed.
  • Auditing an answer. When your assistant reports a number, the table it computed from is right here — open it and see the underlying rows for yourself.
  • Inspecting what feeds a dashboard. Every live tile reads from tables in this pane; if a chart looks off, the pane shows you exactly what the data says.
  • Seeing the whole estate. Nothing is hidden: if your assistant ingested it, it’s browsable here — including team-shared tables, which appear grouped under their team alongside the assistant’s personal folders.
How to use it:
  1. The Data layer sidebar shows the folder tree. Click folders to drill in, exactly like a file directory — Samsara → daily_snapshots.
  2. Open a table (the leaves of the tree) and its rows appear on the right, with a header showing Rows, Loaded, and Columns counts.
  3. Click a column header to sort. Tables load 50 rows at a time — use Load more to page through larger ones.
  4. Use the refresh button after a task run to pull the latest.
The pane is deliberately read-only — a clean window, not an editor. Changing data (fixing a row, adding a column, deleting a table) happens the way everything does: ask your assistant.
The Data pane shows the data layer specifically. Your assistant’s other memory — contacts, transcripts, knowledge, guidance, functions — each have their own dedicated panes in the same Brain section.

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Good to know

  • Honest data only. If a source is down or a fetch fails, your assistant reports the gap — it never fills tables with plausible-looking made-up records.
  • Safe by default. Bulk updates and deletions require explicit, precise instructions; your assistant won’t mass-edit a table on a vague ask. Deletions are permanent, so it confirms before destructive changes.
  • Private by default. Tables live with your assistant. Data that should be shared across a team can be stored in a team’s shared pool instead — say so and your assistant will put it there.
  • Data vs. Knowledge. Structured rows-and-columns material lives here; facts, documents, and policies live in Knowledge. Your assistant routes things to the right place automatically.