Folio 004 · ResultsAnonymized · numbers real

Real work. Real numbers.

We measure every engagement the same way: hours back, dollars saved, decisions made faster. Here’s what that looks like in practice — anonymized, but every number is real.

Client-services teamCustom AI DevelopmentWorkflow Automation
2 daysreturned every month from a single AI agent

One reporting agent gave this team two days a month back.

The challenge

The team was spending roughly two full days every month compiling and writing weekly project-update emails — pulling status out of different tools, formatting it, and sending a tailored summary to each stakeholder.

It was exactly the kind of work that feels like overhead because it is: repetitive, rule-based, and pulling people away from billable client work every single week.

What we built

We built an AI agent that reads their project activity, drafts the weekly summary email for each project in their voice, and hands it over ready to send. A human still reviews and sends every update — the agent does the compiling, formatting, and writing.

No model training, no rip-and-replace of their systems. A well-scoped agent pointed at one painful, repetitive task.

The outcome

The team went from writing updates to glancing at them. That’s two full days a month returned to actual client work — every month, automatically.

Because the win was narrow and measurable, it also built the internal confidence (and the business case) to automate more.

Read the full story on the blog →

Our own back officeWorkflow AutomationStanding Reports
0human touches from line items to reconciled books — generated, pushed to QuickBooks, and verified on a schedule

We automated our own invoicing. A month now closes itself.

The challenge

We host and maintain production apps for three companies, billed monthly. Every month someone had to remember to stamp the line items, generate each invoice, enter it into QuickBooks, email it, and later check what got paid. Classic recurring ritual: small, boring, and easy to miss — and one month we did miss it entirely. Nobody noticed until the books were reviewed.

Worse, the parts that looked automated weren’t trustworthy. Our scheduler reported “succeeded” on a sync job that had actually been failing silently for a month — the job ran, the request behind it was rejected, and the green checkmark never said so.

What we built

The same pipeline we sell as Standing Reports, pointed at our own books. On the 1st, a scheduled job stamps the month’s line items. On the 2nd, invoices generate themselves — idempotently, one per customer per month, so a re-run can never double-bill. They push to QuickBooks with payment buttons attached, and a nightly job pulls payments back so the books reconcile without anyone rekeying.

Then the part most automation skips: monitoring that reads receipts, not statuses. Every scheduled run is verified against the actual HTTP response it produced, because that’s how we caught the sync that had been quietly failing behind a green checkmark. And sends stay behind a human approval gate for the first cycles — automation earns trust before it gets autonomy.

The outcome

Invoicing went from a founder’s monthly memory task to a schedule with evidence: line items on the 1st, invoices on the 2nd, payments reconciling nightly, and a watchdog that alarms loudly instead of failing politely. The month that used to depend on remembering now closes itself.

This is the exact machinery behind Standing Reports — we run our company on the thing we sell. If your team has an invoice run, a weekly packet, or any ritual someone assembles by hand, this is what it looks like when it runs itself: monitored, reversible, and honest about when a human should still click the button.

Read the full build, including what broke →

Want a number like this of your own?

Tell us the workflow that hurts — honest verdict, free.

Get a free fit check →