The weekly report that builds itself. Data pulled from the systems you already use, reconciled, checked for anomalies and delivered with a short written summary, so the Monday meeting starts with the discussion instead of the spreadsheet.
The problem
Every Friday afternoon someone exports pipeline from the CRM, revenue from the accounting system, spend from two ad platforms and ticket counts from the helpdesk, pastes them into a spreadsheet, fixes the formulas that broke, and writes three bullet points. It takes half a day, it is late when that person is on holiday, and by the time anyone reads it the numbers are a week old.
A dashboard tool alone does not fix this. The work is in the collecting, reconciling and explaining, not the charting.
What good looks like
- Figures refreshed on schedule from source systems, not from copies
- Definitions written down once, so “revenue” means the same thing every week
- Anomalies flagged automatically: a metric outside its normal range, a missing data source
- A plain-language summary that a person reviews before it goes out
Scope
What we automate
Data collection and reconciliation
Dashboard automation
Written summary and anomaly flags
Client and board reporting
The Monday-morning brief
A demonstration using sample data. A marketing agency with 30 staff reports weekly to its leadership team and monthly to twelve clients. The operations manager spends most of Friday assembling both.
- At 06:00 on Monday a schedule triggers the workflow. If any source API is unavailable, the run pauses and alerts the owner instead of reporting partial figures.
- Pipeline is pulled from HubSpot, invoiced revenue from lexoffice, spend from Google and Meta ads, and open tickets from the helpdesk, each using the definitions written in the scope card.
- Figures are reconciled (for example, ad spend by client matched to the client list) and written to a dated snapshot in a Google Sheet that feeds the dashboard.
- Each metric is compared with its twelve-week range. Anything outside is flagged with the raw numbers.
- An AI step drafts a 150-word summary: what moved, what is flagged, what the data cannot explain. It only uses the figures in the snapshot and links each statement to its source cell.
- The operations manager reviews the draft at 08:30, edits if needed, and approves. The brief goes to the leadership channel; the dashboard link is attached.
Credit classification: standard (two credits) for the weekly brief; client reports add one to two credits per template. Fits Solo or Team. More on the example workflows page.
Human checkpoints
- Summaries are reviewed before they go to leadership or clients. The AI describes numbers; it does not decide what they mean for the business.
- Partial data never ships. A missing source stops the run and alerts a person.
- Definitions change by change order. If “active client” changes meaning, it is changed in one place, documented and dated.
- Client-facing reports are approved per client by the account owner.
Tools we connect
Sources: HubSpot, Pipedrive, Salesforce, lexoffice, sevDesk, DATEV exports, Xero, QuickBooks, Shopify, WooCommerce, Google Ads, Meta Ads, LinkedIn Ads, Zendesk, Freshdesk, Google Analytics, Google Sheets and Excel. Destinations: Looker Studio, Power BI, Google Sheets, Notion, Slack, Teams, email and PDF. Names indicate compatibility, not partnership.
Reporting workflows rarely need personal data at all. Where they do (for example, per-customer revenue), we aggregate before any AI step and keep the detail in your systems.
What we measure
- Hours per week spent preparing reports
- Report delivery time and on-time rate
- Reconciliation errors found after publication
- Runs completed without manual intervention
- Summary edits per brief (a proxy for draft quality)
Which package fits
Reporting is a good first workflow: high frequency, clear rules, rarely sensitive. One Workflow Pilot (from €1,250): one weekly report from two sources with a reviewed summary. Solo (€2,750): several sources, a dashboard refresh and anomaly flags. Team (€6,900): department and client reporting with templates, approvals and a monitoring view. Prices excl. VAT and usage; see pricing and how it works.
Questions about reporting automation
Do we need a data warehouse for this?
Not at the scale of most 10–100 person businesses. A versioned Google Sheet, Airtable or a small database is usually enough for a snapshot store. If volume or history grows, we say so and design the step up rather than building it in advance.
Can the AI summary be wrong?
It can misjudge why a number moved, which is why it is reviewed and why every statement links to its source figure. It cannot invent figures, because it is only given the snapshot. Under a care plan we track how often the reviewer edits the draft and tighten the prompt when it drifts.
What if a source system changes its API?
The run fails safely and alerts the owner; no partial report is sent. Within a care plan, connector updates are our job. Without one, the runbook explains how to re-authorise or update each connector, and the support window covers issues in the first weeks.
Can this replace our BI tool?
No, and it does not try to. It feeds your BI tool reliably and adds the parts a dashboard does not do: reconciliation, anomaly flags and a written brief. If you have no BI tool, a shared sheet with charts is often enough to begin.