TL;DR: single-source report, one to two days and $800-1,200. Multi-source with a written summary, three to five days and $1,500-2,500. Running cost is the platform plan. The build is easy; the spec is the hard part, and the reason most of these fail is that nobody wrote down what the report is actually for.
Reporting is the most common thing we are asked to automate, and the most reliably worth it. The pattern is always some version of the same person spending Monday morning pulling CRM data into a sheet, reformatting it, and emailing three people. For years.
This covers internal reports: pipeline, revenue, operations, the things your own team reads. If you run an agency and the reports go to clients, that is a different shape with different economics, and we wrote it up separately in the agency reporting guide.
Why this pays back faster than almost anything else
Reports are ideal automation targets because they are completely consistent: same sources, same format, same recipients, same schedule, no judgment in the mechanical part. A well-built one runs for years untouched.
There is a second return people do not anticipate. A hand-built report is subtly different every week, because the person makes small decisions each time about date ranges and what to exclude. An automated one is computed identically every week, which is the first time the numbers become comparable across weeks. Several clients have found that more valuable than the saved hours.
Step 1: write down what the report is for
Not what is in it. What decision it supports. This takes an hour and it is the step that decides whether the project works.
Then document the mechanics: which metrics, from which tools, what period, what format, what gets highlighted. Most people discover at this point that their report is messier than they believed - two sources that disagree, a column someone adjusts by hand every week, a filter nobody can explain. That is not a delay, that is the actual finding.
Be ruthless here. Half the metrics on a typical weekly report are there because someone asked once, two years ago. Automating those means maintaining them forever. Cut them now, while cutting is free.
Step 2: connect the sources
Make and n8n connect to effectively anything with an API: CRM, payments, analytics, ad platforms, spreadsheets, databases. For each source you define the endpoint, the fields, and the shape you need them in. This is the longest part of the build, typically 60-70% of the time.
Where this part goes over budget:
Date ranges. Two tools each have an opinion about what last week means, and about timezone. Pin both explicitly or your numbers will not reconcile.
Pagination. An endpoint returns the first hundred rows and the report silently understates everything until someone checks.
Rate limits, which only appear once the data set grows past the test.
A tool with no usable API, where the honest answer is a manual export step rather than pretending.
Step 3: assemble, and write the sentence
Raw API output needs cleaning and formatting. A spreadsheet is the usual assembly layer; a document template suits narrative reports and a database suits internal dashboards.
Then the part that turns a data dump into a report: a model writes the interpretation from the numbers. Sales up 12% week on week, driven by the enterprise segment, with the mid-market flat for the third week. One rule makes this safe - the summary may only describe figures present in the data, never infer a cause it was not given. A confident invented explanation in a weekly report is worse than no explanation, because people act on it.
Step 4: delivery, and the part people forget
It runs on a schedule and lands where people already are: a channel, an inbox, a shared page, a one-line summary to a phone with the key number and a link to the detail.
The forgotten part is what happens when the report cannot be built. If a source is down, the report must say so loudly rather than quietly going out with a zero in it. A zero looks like a business result. We have seen a team spend a morning on a crisis that was an expired API token.
Three ways this goes wrong after it works
The build is the easy part. These are what we see six months later.
Nobody reads it. An automated report arriving reliably is easier to ignore than a person handing it to you. If it is not driving a decision, switch it off rather than maintain it.
It drifts out of date. The business changes, the definition of a qualified lead changes, and the report keeps computing the old one. Put a review date on it, the same as you would a document.
It fails silently. Covered above, and it is the expensive one.
What we have built
Representative, to calibrate scope:
Weekly pipeline: deals by stage, new leads, closed, movement against last week
Daily revenue digest: recurring revenue, new subscriptions, churn
Weekly operations summary: order volume, fulfilment time, support ticket count
Monthly cross-tool report pulling three or four systems into one view
Ad performance: spend and return across platforms, with the written summary
Cost and timeline
Single source to a sheet to an inbox: one to two days, $800-1,200. Multi-source with custom formatting and a written summary: three to five days, $1,500-2,500. Running cost is the automation platform, $9-29/month, plus a small model bill if there is a written summary. Payback is immediate for anyone spending more than two hours a week compiling by hand.
If you spend under an hour a week on it, do not automate it. The build will cost more than it returns and you will be maintaining something to save twenty minutes.
Tell us which report you compile by hand and we will say whether it is worth automating and what it would cost. Start with the audit at 2pizza.team/audit, two minutes, no call.
Originally published at 2pizza.team. We build AI and automation systems for small teams - fixed price, two to six weeks. See the work.
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