Service
Workflow automation
Knowing a process is manual and slow is not the hard part. Knowing where it can tolerate error and where it cannot is.
Most automation projects skip the step of classifying which parts of a workflow tolerate AI-driven error and which do not, so the result is either an automation nobody trusts enough to use, or one that quietly makes expensive mistakes at scale.
Both failure modes are avoidable, and both are common.
Method
What we do
Map the current workflow step by step
Including the exception handling nobody wrote down.
Classify each step by tolerance for error
Where a wrong answer costs nothing, and where it costs a customer.
Select the model or system for the task
Not the other way around.
Build guardrails and a human review point
Placed exactly where the cost of error is highest.
Pilot with real volume before full rollout
Synthetic test data does not reveal what production traffic will.
Instrument the automation for after launch
So its performance is visible, not assumed.
Deliverables
What you get
- A documented workflow map with risk classification
- A working automation with defined guardrails
- A monitoring specification
- A rollback plan
Logistics
Typical engagement
| Duration | Participants | Format |
|---|---|---|
| 6 to 10 weeks | Process owner, engineering, a risk or compliance reviewer | Workshops, build, and a production pilot |
Who this is for
- You have a specific workflow with real volume and a known cost of error
Who this isn't for
- The workflow changes every few weeks. Automate the decision logic first, not the process itself