Put an AI Data Scientist on Your Reporting Grind

Data cleaning, dashboard refreshes, ad hoc queries, and recurring reports. Describe the task in one sentence, the agent does it across your apps.

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DATA ANALYST AGENT

Clean and standardize a messy data export in one pass

Merge several exports into one clean dataset

Assemble the weekly metrics report without opening a tab

Answer a one-off data question with the actual number and context

Catch anomalies in a metric before they hit a report

Turn a finished analysis into a plain-language summary

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Any Data Task. One Message. Done.

A messy export to clean, a dashboard to refresh, a quick number someone needs now. Tell the agent what you need and it works across Google Sheets, your warehouse, Slack, and 1,500+ apps.

Get Work Done With Simple Chat Messages

Take the raw export in the 'Raw Import' tab in Google Sheets. Remove duplicate rows, standardize date formats to YYYY-MM-DD, fix inconsistent text casing in the category column, trim whitespace, and flag any rows with missing required fields. Move the cleaned data to a 'Clean' tab and log every change made in a 'Cleaning Log' tab. Post a summary of what was fixed and how many rows were flagged to the data channel in Slack.||

Cleaning complete. 4,812 rows processed, 318 fixed, 27 flagged.

Data Cleaning Summary — Jun 10

Issue	Rows affected	Action
🔴 Duplicate rows	142	Removed
🟡 Inconsistent dates	89	Standardized to YYYY-MM-DD
🟡 Mixed text casing	61	Normalized to title case
🟡 Leading/trailing spaces	26	Trimmed
🔴 Missing required fields	27	Flagged for review

Summary: 4,812 rows processed. 318 cleaned automatically and moved to the 'Clean' tab. 142 duplicates removed. 27 rows are missing a required field (mostly customer ID) and were left in a 'Needs Review' tab rather than guessed at. Full change history is in the 'Cleaning Log' tab. The dataset is ready for analysis once the 27 flagged rows are resolved.

👇 Here's what your team could do with a single message.

1.Clean and standardize a messy data export in one pass

2.Merge several exports into one clean dataset

Take the three exports in the import folder in Google Drive (CRM contacts, billing records, and product usage). Match them on the shared customer ID, merge into a single table, and flag any ID that appears in one source but not the others. Standardize the column names across all three. Save the merged dataset to a 'Master' tab in Google Sheets and post a note to the data channel in Slack with the match rate and any unmatched records.

3.Profile a new dataset before you trust it

Take the dataset in the 'New Data' tab in Google Sheets. For each column, report the data type, percent missing, number of unique values, min and max for numbers, and the most common values. Flag any column that looks suspicious (all nulls, a single repeated value, or out-of-range numbers). Write the profile to a 'Data Profile' tab in Google Sheets and post the red flags to the data channel in Slack.

1.Assemble the weekly metrics report without opening a tab

Pull this week's numbers from the source tabs in Google Sheets and your data warehouse: signups, active users, revenue, and churn. Compare each against last week and the 4-week average, calculate the change, and flag anything that moved more than 10 percent. Write a clean weekly report with the key numbers and a short read on what moved. Update the 'Weekly Report' tab in Google Sheets and post the summary to the data channel in Slack.

2.Refresh a dashboard's underlying data and check it held

Pull the latest data from the source tables for the metrics dashboard and update the feeder tabs in Google Sheets. After refreshing, check that row counts grew as expected, no key metric went null, and the totals reconcile against the source. If anything looks off, flag it instead of publishing. Post a refresh-complete note with the new totals to the data channel in Slack, or an alert if a check failed.

3.Rebuild a recurring report against new numbers

Take last month's report template in Google Docs. Pull the current month's figures from the source tabs in Google Sheets, drop them into the same structure, recalculate every comparison and growth rate, and rewrite the commentary to reflect what actually changed. Save the new report to Google Docs and email it to the stakeholder list via Gmail.

1.Answer a one-off data question with the actual number and context

Someone asked: [paste the question]. Figure out which source tables in Google Sheets or your data warehouse hold the answer, pull the relevant data, run the calculation, and sanity-check it against a related number so you know it's right. Give the answer with the figure, the time period, and one line of context on whether it's normal or notable. Post the answer to the person in Slack.

2.Break a metric down by segment to find what's driving it

Take the metric [name it] for the last [period]. Pull the underlying data from the source tabs in Google Sheets and break it down by the key segments (plan, region, channel, cohort). Identify which segment is driving the overall movement and by how much. Write the breakdown to an analysis tab in Google Sheets with a short read on the main driver, and post the takeaway to the data channel in Slack.

3.Run a quick cohort or retention cut on demand

Pull the signup and activity data from the source tabs in Google Sheets for the last 6 months. Group users into monthly signup cohorts and calculate retention at week 1, week 4, and week 12 for each. Flag any cohort whose retention is notably below the others. Write the cohort table to an analysis tab in Google Sheets and post the headline retention trend to the data channel in Slack.

1.Catch anomalies in a metric before they hit a report

Pull the daily values for [metric] over the last 90 days from the source tabs in Google Sheets. Calculate the normal range, then check today's value against it. If today's number falls outside the expected range, flag it, note how far off it is, and check the related metrics to see if it's a real change or a data issue. Log the result in a 'Anomaly Watch' tab in Google Sheets and post an alert to the data channel in Slack if something's off.

2.Reconcile two sources that should agree but don't

Pull the revenue figure from the billing export and from the finance summary tab in Google Sheets for last month. Compare them line by line, calculate the total difference, and identify which records or categories account for the gap. Write the reconciliation to a 'Recon' tab in Google Sheets showing the matched and mismatched amounts, and post the size of the gap and the likely cause to the data channel in Slack.

3.Verify a pipeline actually loaded what it should have

Check the source tables that feed the daily reporting in Google Sheets and your data warehouse. Confirm each one updated today, the row counts grew within the normal range, and no key column came in fully null. Flag any table that's stale, short on rows, or missing data. Log the health check in a 'Pipeline Health' tab in Google Sheets and post a pass-or-fail summary to the data channel in Slack.

1.Turn a finished analysis into a plain-language summary

Take the analysis in the 'Results' tab in Google Sheets. Write a plain-language summary for a non-technical stakeholder: what the data shows, why it matters, and what you'd recommend doing about it. Skip the methodology and lead with the takeaway. Save the summary to a Google Doc and post the headline finding to the data channel in Slack.

2.Explain why a metric moved, not just that it moved

The metric [name it] changed by [amount] this period. Pull the underlying data from the source tabs in Google Sheets, break the change down by segment and by contributing factor, and figure out what actually drove it. Write a short explanation that a stakeholder can act on, with the main driver up front. Save it to a Google Doc and post the summary to the data channel in Slack.

3.Build a monthly insights digest from the raw numbers

Pull the month's key metrics from the source tabs in Google Sheets and your data warehouse. Identify the three most important changes, what drove each, and what they suggest for next month. Write a tight insights digest in Google Docs that leads with the takeaways, not the charts. Email it to the stakeholder list via Gmail and post the highlights to the data channel in Slack.

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Set It Once. The Reporting Runs Itself.

Daily pipeline checks, weekly reports, anomaly alerts, dashboard refreshes. Running on schedule and on trigger whether you're at your desk or not.

Automate recurring processes in 30 seconds.

Run the daily pipeline health check before anyone's online

When this happens...

App

Clock

When

Every weekday at 06:30 AM

Then do this...

👇 No workflow builder. Set it up in plain English.

Run the daily pipeline health check before anyone's online

Every weekday at 06:30 AM

Check the source tables that feed the daily reporting in Google Sheets and your data warehouse. Confirm each updated overnight, row counts grew within the normal range, and no key column came in null. Flag anything stale or short on rows. Log the result in a 'Pipeline Health' tab in Google Sheets and post a pass-or-fail summary to the data channel in Slack so a broken feed gets caught before the reports go out.

Send the weekly metrics report before the Monday standup

Every Monday at 08:00 AM

Pull last week's signups, active users, revenue, and churn from the source tabs in Google Sheets and your data warehouse. Compare each against the prior week and the 4-week average and flag anything that moved more than 10 percent. Write a clean weekly report, update the 'Weekly Report' tab in Google Sheets, and post the summary to the data channel in Slack.

Alert the team the moment a key metric goes out of range

Every weekday at 07:00 AM

Pull the latest daily values for the tracked metrics in Google Sheets. Compare each against its normal 90-day range. For any metric that fell outside its range, check related metrics to judge whether it's a real move or a data issue. Post an alert to the data channel in Slack with the metric, the value, and the likely cause.

Refresh the dashboards and confirm the refresh held

Every weekday at 07:30 AM

Pull the latest data into the feeder tabs for the metrics dashboards in Google Sheets. After refreshing, check that row counts grew as expected, no metric went null, and totals reconcile against the source. Post a refresh-complete note with the new totals to the data channel in Slack, or an alert if a check failed instead of publishing bad numbers.

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Data Playbooks Anyone on Your Team Can Run

Data cleaning, recurring reports, anomaly checks, request intake. Same process, same rigor, every single time.

Complete repetitive processes in clicks

Run a full clean and prep on a raw dataset

1. Dataset & Rules

Dataset & Rules

Fill fields below 👇

Source Tab or File *

Required Fields *

Date Format to Standardize To

2. Clean and Validate the Data

Agent

Read the raw data in Source Tab or File. Remove duplicate rows, standardize all dates to Date Format to Standardize To if given otherwise YYYY-MM-DD, normalize text casing, and trim whitespace. Flag any row missing one of the Required Fields rather than filling it in. Record every change made with the row and the action taken, and count how many rows were cleaned, removed, and flagged.

3. Write Cleaned Data and Log to Google Sheets

Add Rows to SheetinGoogle Sheets

4. Post Cleaning Summary to Slack

Send MessageinSlack

👇 See use cases.

1.Run a full clean and prep on a raw dataset

How this Playbook works?

Enter the source tab or file and the required fields every row must have. The AI agent reads the raw data, removes duplicates, standardizes dates and text casing, trims whitespace, and flags any row missing a required field rather than guessing at it. The cleaned data gets written to a 'Clean' tab in Google Sheets with every change recorded in a 'Cleaning Log' tab, and a summary of what was fixed and how many rows were flagged gets posted to the data channel in Slack.

2.Build a recurring report from a template and fresh data

How this Playbook works?

Enter the report template and the period to report on. The AI agent pulls the period's figures from the source tabs in Google Sheets and your data warehouse, drops them into the template structure, recalculates every comparison and growth rate, and rewrites the commentary to reflect what actually changed. The finished report gets saved to a Google Doc and emailed to the stakeholder list via Gmail, with a short heads-up posted to the data channel in Slack.

3.Run a segment deep-dive on a metric that moved

How this Playbook works?

Enter the metric and the period to analyze. The AI agent pulls the underlying data from the source tabs in Google Sheets, breaks the metric down by plan, region, channel, and cohort, and identifies which segment is driving the overall movement and by how much. It writes a clear read on the main driver. The breakdown gets written to an analysis tab in Google Sheets, and the takeaway gets posted to the data channel in Slack.

4.Set up an anomaly check for a key metric

How this Playbook works?

Enter the metric and how many days of history to baseline against. The AI agent pulls the metric's recent values from the source tabs in Google Sheets, calculates its normal range, and compares the latest value against it. When the value falls outside the range, it checks related metrics to judge whether it's a real change or a data problem. The result gets logged in an 'Anomaly Watch' tab in Google Sheets, and an alert with the value and likely cause gets posted to the data channel in Slack.

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Less Cleaning and Refreshing. More Real Analysis.

Describe your data task in one sentence. The agent does it across your apps.

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