Weekly reports, written in plain language,
not assembled from screenshots
A weekly report usually means someone spending an afternoon pulling numbers from four dashboards into a deck. The bullet points just repeat what the dashboards already show. Our agent pulls the same numbers from your actual warehouse. It writes what changed and why it matters, in sentences, not screenshots.
Why Friday afternoon disappears into a deck
Someone on most teams spends part of their Friday, or their Monday morning, assembling a weekly report. Opening three or four dashboards. Screenshotting a chart or two. Copying a handful of numbers into a slide. Writing two or three sentences, “sales were up,” “spend was flat.” The person doing this is often not the one who would interpret the numbers best. They just got assigned the recurring task.
The report that results reads like a status update, not something that actually helps a decision. A number moved, but whether that move is meaningfully outside the normal range, or just ordinary week-to-week noise, rarely gets stated explicitly. Nobody has time to calculate a proper baseline by hand every week. Readers either over-react to normal variation, or start skimming without really absorbing it, because three months of “sales were up slightly” trains people to stop reading closely.
There is a consistency problem too. The format drifts depending on who builds the report that week. Which metrics get included depends on what felt important at the time. Comparing this month’s report to three months ago is harder than it should be, because the structure is not quite the same. A report meant to build a record over time ends up as a series of one-off snapshots instead.
How the agent writes the report
It connects to the same data your dashboards already use, your warehouse, your BI tool, or a spreadsheet if that is genuinely where the numbers live. It pulls the figures for the metrics your team has agreed matter every week. Instead of listing raw numbers, it writes a short narrative for each one. What the number is. How it compares to last week and to a longer baseline. Whether that move falls inside or outside what is normal for that metric, based on its own history.
Anything meaningfully outside the normal range gets a call-out at the top of the report, rather than sitting buried in the same paragraph as routine numbers. A reader with only thirty seconds still sees the one thing that actually changed. Where a plausible explanation shows up in the data itself, a spend change lining up with a metric move, say, the report states it as a hypothesis. Tied to the specific numbers behind it, never stated as a confident conclusion. The real judgment call stays with the person who reads it.
The format stays consistent week over week on purpose, so a report from this month reads the same way as one from three months ago. A reader builds a mental model of what to expect instead of re-learning the structure every time. Every number traces back to the query that produced it. Anyone can check it, which matters most for the weeks where a number looks surprising.
Delivery happens on whatever schedule and channel your team actually uses. Email for a formal distribution list. Slack or Telegram for a faster-moving team. The report lands where people are already paying attention, not in an inbox folder nobody opens on Fridays.
What stays with your team
Deciding which metrics belong in the weekly report stays with the person who owns that part of the business. So does judging whether a flagged anomaly needs a real response or is explainable noise, and drawing any strategic conclusion from a trend across several weeks. The agent writes the narrative. A person decides what to do about it.
Safeguards built in
Every number in the report comes from a live query against your own data, shown alongside the narrative, so nothing is a black-box figure. The normal range used to flag anomalies comes from your own metric history, not a generic threshold. It gets reviewed and adjusted after the first month of real reports. The report format and metric list only change when your team asks for a change, never automatically. A report from six months ago stays directly comparable to this week’s.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $600 | One data source, one weekly narrative report, fixed format | 4 to 10 days |
| Department package | from $2,500 | Reporting across several sources plus dashboard commentary and spreadsheet automation for the team | 2 to 4 weeks |
Running cost is usually $10 to $50 a month in model usage depending on report length and data volume, with a budget cap set before launch.
Related
This pairs closely with dashboard commentary for a shorter, always-on version of the same idea. Survey analysis and market research summaries fit too, when the weekly report needs qualitative input alongside the numbers. See the automation-everything overview and the AI agents service page for the broader catalogue. The two-brand analytics warehouse with an AI analyst in Telegram answers exactly this kind of question on demand. It shows what the underlying numbers look like once they are joined properly.
If Friday afternoons currently disappear into building a report nobody fully trusts, get in touch and we will connect it to your real data instead.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does automated plain-language reporting cost?
From $600 to connect one data source and generate a weekly report in a fixed format. Pulling from several sources (ads, sales, product usage) into one combined narrative report is $1,500 and up.
How long does it take to set up?
4 to 10 days. Most of it goes into agreeing which metrics matter enough to narrate every week, and what counts as a normal range for each. That way the first few reports do not need a rewrite.
Which tools does it connect to?
Your existing warehouse or BI tool: PostgreSQL, BigQuery, Looker or Metabase. Ad platforms and CRM exports too, or a plain spreadsheet if that is what you actually have. Delivery goes to email, Slack or Telegram.
What if the AI gets a number or a conclusion wrong?
Every figure in the report is generated from a visible query against your own data, shown alongside the narrative. A wrong number is checkable in seconds, not trusted blindly. Judgment calls about why something changed are phrased as a hypothesis with supporting data, never stated as fact.
Is our business data exposed anywhere?
The report reads from your own warehouse or tool with a scoped, read-only connection, and the generated report goes only to the recipients you choose. We do not store a separate copy of your business data outside the report pipeline itself.