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AI workflowsand automations

Internal AI that gives a team back its week.

03 of 17What gets built. Custom systems, designed around how your business actually runs.

01. What it is

What ai workflows and automations means here

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.

The layers,pulled apart

  1. 01

    Interface

    A purpose-built screen for the team that runs the workflow.

  2. 02

    Model

    Prompts, models and guardrails chosen for the job.

  3. 03

    Integrations

    Connected to the tools where the work already lives.

Assembled, it gives the week back.

MONTUEWEDTHUFRIHOURS BACK0102CALMAILCRM03

02. Who it is for

Who thisis for

Is this you
  1. 01Teams where the same five-hour task happens every week.
  2. 02Firms with knowledge work that follows a recognizable shape and could be drafted by a model and edited by a human.
  3. 03Operators who've tried generic AI tools and found them too generic.

03. What you get

What you get

  • 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

04. Questions

What people askfirst

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.

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.

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.

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.

Build thiswith us

Thirty minutes, free. Tell us what is slow, messy or missing and leave with sketches and a two-week build estimate.