Let the morning reports build themselves.

Most companies have someone who starts the week by downloading three CSVs, pasting them into a workbook and emailing it around before the 9 o’clock meeting. We’ve met a lot of them, and we built this so they can stop.

Pipelines and automations do that part now. Your data syncs overnight, the models rebuild, an agent checks the numbers, and the report is waiting in Slack when people get in. The person who used to build it can spend Monday on something else.

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What a scheduled morning run looks like

An automation is a set of stages. Tasks in a stage run together; stages run in order, so the agent only reads numbers that are already fresh. We built it that way after seeing too many “insights” written about yesterday’s half-loaded data. Here’s what a typical morning looks like.

One morning of automations, 6:00 to 7:00 AM Two syncs start at 6:00 and run in parallel, the finance models rebuild at 6:14, two agents check the numbers at 6:22, and the summary lands in Slack at 6:27, more than half an hour before anyone opens it at 7:00. Illustrative. 6:00 6:15 6:30 6:45 7:00 Salesforce sync Salesforce sync (Pipeline): 6:00 to 6:14, 14 min 14 min NetSuite sync NetSuite sync (Pipeline): 6:00 to 6:11, 11 min 11 min Rebuild the finance models Rebuild the finance models (dbt): 6:14 to 6:22, 8 min 8 min Revenue anomaly check Revenue anomaly check (Agent): 6:22 to 6:25, 3 min 3 min Data-quality sweep Data-quality sweep (Agent): 6:22 to 6:27, 5 min 5 min Exec summary to Slack Exec summary to Slack (Delivery): 6:27 to 6:28, 1 min 1 min 7:00 · you open Slack, and it is already there
Illustrative: a typical morning run. Hover a bar for its times.
See the numbers
TaskTypeStartsEndsMinutes
Salesforce syncPipeline6:006:1414
NetSuite syncPipeline6:006:1111
Rebuild the finance modelsdbt6:146:228
Revenue anomaly checkAgent6:226:253
Data-quality sweepAgent6:226:275
Exec summary to SlackDelivery6:276:281

Three people who stop doing it by hand

The analyst I spend every Monday rebuilding the same spreadsheet.

Pipelines keep the data current on a schedule, with history and change tracking. The spreadsheet becomes a Data App that’s always up to date.

The operations lead I find out a feed broke when the CEO asks why the number looks wrong.

Every run is logged, and failed steps retry on their own. When something does fail, it explains what went wrong in plain English, usually before anyone opens the dashboard.

The finance manager Can the report just arrive?

It can. Delivery sends the summary, the charts or the spreadsheet to email, Slack or Teams on your schedule.

Syncs, SQL, agents and delivery, all in one automation

Pick from the menu and drop it into a stage. There are no scripts to maintain and no second scheduler to keep in sync. We kept every job in one place because jobs split across two schedulers tend to fail in the gap between them.

  • Bring data inPipelines from 75+ sources, file drops, unzip and copy jobs.
  • Shape itSQL scripts, dbt projects, notebooks and semantic models.
  • Think about itAgents that check and explain, forecasts, and OCR that turns a folder of PDFs into a table.
  • Send it onReports to email, Slack or Teams, and Databricks jobs if you already run them.
The automation task menu: Databasin tasks such as Pipeline, File Drop, SQL Script, Notebook and SQL Report; dbt; intelligence tasks LLM Agent, Forecast and OCR; and Databricks tasks. The task menu. Each one is a building block for a stage.

See every job and how its last run went

Each automation shows when it last ran and whether it worked. Runs retry on their own, every task keeps its own log, and every version of the automation is saved, so you can always see what changed and when.

Run it on a schedule, on a trigger, or once by hand while you test it. One limit to plan for: an automation is only as fresh as its slowest source, so a vendor API that updates once a day still updates once a day.

A list of automations, each card showing its task type and whether its last run succeeded. Seven automations, each with its last run on the card.

For the technical readerHow pipelines workEvery task typeAgents

Tell us about your Monday report, and we’ll help you automate it.

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