By use case: Weekly reporting
Automated weekly reporting
The numbers from every tool in one place, and a written summary waiting on Monday morning.
- First workflow live
- Nightly pull, usually inside the first sprint
- Usually owned by
- Founders and managing directors who want one weekly view
- Built on
- Supabase, BigQuery, Looker Studio
- Judged on
- Time to build the report and Report on time
In short
Automated weekly reporting pulls figures from the tools your business runs on, such as the CRM, accounting, ads, helpdesk and production systems, into one store, shows them on a single dashboard, and drafts a plain-language summary of what changed and why it might matter. SBD builds the data pipeline first, because the summary is only as good as the numbers under it, then adds the AI summary with a named person who reads it before it goes out.
Who this is for
The people who usually own weekly reporting.
- Founders and managing directors who want one weekly view
- Operations and finance leads who build the report today
- Team leads who need their own numbers without asking
- Boards and investors who receive a monthly update
Is this you?
If three of these sound like your week, this page is about your team.
- Someone spends Monday morning building the report
- Two people bring different numbers to the same meeting
- The report stops when one person is away
- Nobody writes down what the numbers mean
- A BI tool was bought and few people open it
What it costs today
Where weekly reporting loses the hours.
Monday is spent building the report
Someone exports from five tools, pastes into a spreadsheet and fixes the formulas that broke since last week.
The same metric, three answers
Revenue in the CRM, revenue in accounting and revenue in the spreadsheet disagree, and the meeting is spent on which is right.
Numbers without a story
A wall of figures goes out, and nobody says what moved, what is unusual or what needs a decision.
The report depends on one person
When they are away, there is no report, or a different one.
For leadership
Read the week, do not build it.
Send us last week's report and the tools it came from. We will show you which numbers can be automated, which definitions disagree, and what a summary would say.
Show us your report
By company size
Weekly reporting at 10 people, and at 1,000.
- 1 to 10
The founder checks five apps on Sunday night to know how the week went.
- 11 to 50
A spreadsheet report one person builds by hand, late when they are busy.
- 51 to 200
Each team reports its own way, and the leadership meeting argues over whose numbers are right.
- 201 to 1,000
A BI tool few people open, analysts fielding the same requests, and no written summary of what changed.
What we build
What we build for weekly reporting teams.
- 01
Connectors into one store
Scheduled pulls from each tool into a database you own, with the definitions written down, so a metric means the same thing every week.
- 02
One dashboard
A dashboard by team or role, built in Looker Studio, Metabase or a custom app, with filters people actually use.
- 03
AI-written weekly summary
An AI step compares the week to the last few, writes what changed in plain language, and cites the numbers. A named person reviews and sends.
- 04
Checks before anything goes out
Missing data, a failed pull or a figure far outside its usual range stops the report and alerts the owner instead of sending a wrong number.
The workflows
The weekly reporting workflows, and what sets each one off.
- 01 Nightly pull
When a schedule
ThenData pulled from each tool into the store and checked
- 02 Dashboard refresh
When the pull completes
ThenDashboards updated with the latest figures
- 03 Draft summary
When monday morning
ThenAI writes what changed, citing figures, and sends it to the owner
- 04 Review and send
When the owner approves the draft
ThenSummary sent by email or Slack with the dashboard link
- 05 Data alert
When a pull fails or a figure is far out of range
ThenReport held and the owner alerted
The numbers we move
How a weekly reporting build is judged.
Every build has a number written into the blueprint. These are the ones weekly reporting builds are measured by, and how we measure them.
| Metric | Before | After | How it is measured |
|---|---|---|---|
| Time to build the report | Hours every week | A review, not a build | Time logged by the report owner on the weekly report |
| Report on time | Depends on one person | Every week | Weeks the report went out by the agreed time, per quarter |
| Disputed figures | Common in meetings | Rare, with one definition | Corrections raised on the report after sending, per month |
| Failed data pulls caught | Found when a number looked odd | Caught before sending | Alerts from the pre-send checks, per month |
Systems we connect in weekly reporting
What is usually already there, and what we add.
Anything with an API or a database we can read works the same way. Each tool links to its page in the integrations directory.
-
HubSpot -
Salesforce -
Xero - QuickBooks
- GA Google Ads
- Google Analytics
- Shopify
-
Zendesk
Proof
Proof from builds like this.
- Customer story
Multi-user production dashboard
A multi-user dashboard giving a copper wire manufacturer live visibility of every production stage, replacing scattered manual tracking.
Read the story - Customer story
Sales channel inventory sync
One control panel showing what is live where across Shopify, eBay and Chrono24, where before there was no single view.
Read the story
How it starts
An audit of the weekly reporting work your team still does by hand.
One to two weeks. We map the workflows, put an hours-a-week figure on each, and give you a costed blueprint in order of payback. The audit is credited in full to the build.
- Week 1 to 2Audit. We sit with the people who do the work and map it.
- Week 3Blueprint. Fixed scope, fixed price, the first build chosen by payback.
- Week 4 onBuild in two-week sprints, working software every second Friday.
- After go-liveSixty days of hypercare, then a care plan or run it yourself.
What this does not do
So the scope is clear before the call.
- One tool with good built-in reports. Use those first.
- Metrics nobody acts on. If no decision follows a number, we will suggest dropping it.
- An AI summary that sends without a person reading it. We keep someone on the send.
Terms, defined
Weekly reporting terms, in plain English.
- Metric definition
- A written rule for how a number is calculated and from which source.
- Data consolidation
- Bringing data from several tools into one store with consistent definitions.
- Pre-send check
- A test that stops a report if data is missing or a figure is implausible.
Questions
What people ask about weekly reporting automation.
Is this the same as data consolidation?
Consolidation is the foundation: getting your tools into one place with agreed definitions. Weekly reporting sits on top of it. Our data consolidation and dashboards services pages cover each part in more depth.
Will the AI summary make things up?
It writes only from the figures we pass it, and every claim cites a number from the dashboard. A person reads it before it is sent. If the data checks fail, there is no summary that week, just an alert.
Which dashboard tool do you use?
Whichever fits your team. Looker Studio and Metabase cover most needs at low cost. A custom dashboard makes sense when people also need to act from it.
Our data is messy. Is that a problem?
It is normal. The audit shows where definitions disagree and which sources can be trusted. We fix definitions before we automate the report.
How much does it cost?
Start with the $500 Automation Audit, credited to the build. Dashboards start at $3,000, with the fixed price set after the audit.
Reading
- Strategy
Automation vs AI systems: what moves the number
Stacking task automations stalls at around twenty. The difference between a task and a system, the five layers that hold it up, and what to rebuild first.
- Operations
Who noticed when the workflow stopped?
Most automation failures are silent: the run shows green and nothing happens for weeks. How it happens in Zapier and n8n, its cost, and the four checks we add.
- Strategy
The 95% figure, read properly
MIT says 95% of generative AI pilots show no profit impact. What the study of 52 interviews and 153 surveys found, what it did not, and what the other 5% did.
Related
- Function
Leadership
The numbers you run the company on, pulled from the systems that hold them, every morning, with a note on what changed.
- Function
Operations
The order moves through the business without anyone re-typing it, and everyone who needs to know, knows.
- Industry
Manufacturing
Production entered once at the machine. Stock that matches the shelf. Dispatch paperwork and customer messages that send themselves. All of it live this morning, not typed up tonight.
- Use case
Order to cash
Orders entered once, fulfilment visible to everyone, invoices raised the moment goods leave, payments matched as they land and reminders sent before anyone has to ask.
By use case: Invoice processing, Lead routing, Customer onboarding, Employee onboarding, Quote generation, Order to cash, Ticket triage, Document extraction, Approval workflows, Inventory sync, Client portals
Weekly reporting
Get the weekly reporting hours back.
Bring the one weekly reporting workflow your team still runs by hand. You leave the call with a yes or no on fit and the first thing we would build.
Book the call