AI Workflows
Applying language models to work your team repeats. Drafting, sorting, extracting, summarizing, and routing, built as something reliable enough to depend on.
Who it is for
Small teams doing high volume manual work. Operators who tried a chatbot, saw the potential, and could not make it repeatable.
The problem it solves
Most AI pilots stall because a demo is not a workflow. Getting a good answer once is easy. Getting it every time, checked, and inside the process where the work happens is the actual job.
What I do
- 01Pick the step where the time actually goes instead of the most impressive demo.
- 02Write and version the prompts as part of the codebase, so behavior can be changed on purpose.
- 03Put a human approval gate anywhere a mistake would reach a client.
- 04Log what ran, so a bad output can be traced instead of guessed at.
What you end up with
- A working automation inside your process, not beside it.
- Versioned prompts and the reasoning behind them.
- Logging and an approval gate where the risk sits.
04Also in AI & Growth
- Search & LaunchFindable, shareable, and properly handed over.
- AI VisibilityBeing found inside AI answers, not only in blue links.
- Internal ToolsSmall custom software for the work you do daily.
- Technical AuditsA straight read on what you have and what it is costing you.
- Standing CareThe craft kept, after the launch.