Case Studies

Built in weeks. Running in production.

Real builds solving real problems, with the numbers attached. Client names are withheld under confidentiality. Everything else is how it actually happened.

Why these read differently

Most AI case studies end at the launch. Ours don't.

Because for us, launch is the start of the part that matters.

Real numbers, honestly rounded

Hours saved, channels covered, days to deliver: all measured from the running tools, and rounded rather than inflated. No "up to 60% efficiency gains" with an asterisk.

Delivery times stated, not implied

Days or weeks: we tell you which, for every build. If a tool took days, we say days. Fixed scope makes that possible.

Every one is running in production

Each build on this page is running right now, monitored and maintained by the team that built it. A case study about a tool that quietly died six months later isn't a case study. It's an obituary.

Tell us about the process that's eating your week.

A 30 minute scoping call. No pitch deck, no obligation. Just an honest answer on whether we can fix it.

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