Automation builds we've shipped.

Every build below started the same way: a process someone was doing by hand, every week, that nobody had ever costed. That is usually where automation pays — not in the exotic AI use case, but in the recurring manual work that has quietly become part of the job description.

We scope these the same way each time. Map the process as it actually runs, not as the org chart says it runs. Count the hours and the error rate. Build the smallest system that removes the manual step without removing the human judgment. Then attach monitoring, because an automation nobody is watching is a failure waiting to happen quietly — and a silent failure in a finance process is worse than the manual work it replaced.

Two of the three builds here came out of finance and back-office work, which is not a coincidence: it is where the repetition lives, where the errors cost real money, and where we know the process well enough to automate it safely. If you want the broader picture of how we approach this, start with AI & Systems. If the underlying problem is that your numbers can't be trusted in the first place, that is a fractional controller problem before it is an automation one.

Client names are anonymized at their request, and the workflow diagrams are illustrative recreations rather than screenshots of production systems. The numbers are real.