01 Build
AI workflows and automations
Internal AI that gives a team back its week.
What it is
AI workflows are internal tools that use language models to compress recurring work. Document review, intake processing, research synthesis, content drafting against a house style. We scope what work the AI should and shouldn't do, build the workflow around it, and ship it to the team that runs it. The output is hours back on the calendar.
Who it's for
Teams where the same five-hour task happens every week. Firms with knowledge work that follows a recognizable shape and could be drafted by a model and edited by a human. Operators who've tried generic AI tools and found them too generic.
What's included
- Workflow audit and AI suitability assessment
- Model selection and prompt design
- Custom interface for the team that runs it
- Evaluation against a measurable baseline
- Integration with existing tools where it earns its place
- Maintenance window for prompt drift and model updates
Recent proof
FAQ
- Is this a chatbot?
- No. The interface is whatever the work calls for. Sometimes it looks like a form. Sometimes it looks like a queue. Sometimes it looks like a document review screen. We build the interface around what the team needs to do, not around what conversational AI happens to look like right now.
- Which models do you use?
- Depends on the work. Claude for most reasoning and writing tasks, OpenAI for some structured outputs, smaller open-source models where cost and latency matter. We choose the model for the job, not the other way around.
- What about data privacy?
- We use API tiers with zero-data-retention where the engagement calls for it. We don't train models on client data. We're explicit in the build doc about what data goes where.
- How do you measure whether the AI is actually working?
- Against a baseline we capture before the build. If the workflow took 5 hours and now takes 90 minutes, that's the win. If it doesn't improve, we either fix it or kill it.