Use cases
What teams use Rafter for
The pattern is always the same: something your team knows lives in one place it shouldn’t — one head, one chat history — and Rafter moves it somewhere every teammate’s AI can reach. Three ways that plays out:
Turn workflows into skills
One teammate figures it out — everyone's AI can run it.
Read →Stop repeating yourself
One team memory, read by every AI tool.
Read →Who gets the most out of it
The teams Rafter helps most have a specific shape: several people doing similar work, each with their own AI tool open, and a body of know-how that currently lives in people’s heads and old chat threads. Advisory firms, agencies, accounting and law practices, and small product teams all hit this — the work is repeatable, but the knowledge of how it is done is not written anywhere a tool can reach.
The tell is simple: if two teammates would get materially different answers from their assistants to the same question about how your firm works, the knowledge is not shared yet — it is just duplicated, unevenly.
Where teams start
Not with a migration. The useful first move is to catch one thing that already happened:
- The next time you explain something twice.Save the second explanation as a memory. You have just stopped explaining it a third time.
- The next time a teammate does something well.Ask them to save it as a skill. It is now the team’s way of doing it, not theirs.
- The next time a decision gets re-litigated.Write down what was decided and why. The next assistant to be asked will find it instead of guessing.
Nothing has to be complete to be useful. A dozen memories is already more shared context than most teams’ AI tools have today.
Convinced enough to try it in your own tool?From $29 per user — see pricing.
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