AI automation
Decide where AI acts—and where a person must.
Bring one repeated workflow. We map the current workflow, its boundaries, exceptions, human decisions and evidence gaps before recommending automation.
Capability · workflow model
One workflow. Seven explicit boundaries.
- 01
Input
What starts the work?
- 02
Context
Which approved information is needed?
- 03
AI action
What may AI draft, classify, extract or recommend?
- 04
Human approval
Who decides before a higher-risk action?
- 05
System update
Which tool receives the approved outcome?
- 06
Exception path
Who handles missing context, low confidence or failure?
- 07
Observable record
What is logged, reviewed and recoverable?
Connect the useful systems. Keep the boundary visible.
We document the systems, data flow, approvals, logs, failure handling and recovery paths required by the workflow.
Bound the AI step before choosing the model.
We define the AI task, allowed context, expected output, evaluation examples and the conditions that route work to a person before choosing a model.
Put accountability at the decision point.
We name what AI may do, what a person approves and where exceptions go.
Leave more than a working demo.
The delivery method includes evaluation criteria, logs, error handling, recovery steps, a playbook, a named owner and a review cadence.
A production result is not implied before separate release and observation evidence exists.
Start where the work is observable.
A useful candidate has one named workflow and accountable owner; observable repeated volume or friction; known users and source systems; safely handled representative data; a human approval or exception point; and evidence defined before build.
A generic chatbot, fully autonomous high-stakes decision or guaranteed ROI brief is not a fit.
Bring one workflow and its messy edges.
Share the tools, handoffs, exceptions and decisions. Discovery will determine whether to build, simplify the process or stop.