Classification
Label messages and route them to the right queue.
Applied AI with control
AI automation for operations and documents
I design workflows that use AI to interpret text or documents when a rule is not enough, with validation, traceability and human review where errors matter.
Problem it solves
Reduce repetitive work without hiding uncertainty
AI can classify, extract and prepare, but it does not turn a probable answer into reliable data by itself.
The workflow must decide what can be automated, what is validated and what reaches a person.
Investment decision
When it is worth it
Many similar emails or documents are reviewed.
Fields must be extracted from partly unstructured text.
The team classifies requests before working on them.
A draft saves time while a person retains the decision.
Frequent work
Common use cases
Scope should start with an observable problem and a clear way to verify the improvement.
Extraction
Prepare structured fields from documents with validation.
Drafts
Responses, summaries or reports for human review.
Internal search
Query documentation with sources and visible limits.
Possible scope
What it can include
- Input, preparation and validation flow.
- Integration with models and current tools.
- Thresholds, human review and exception handling.
- Quality, cost and operational logging.
Working criteria
What to avoid
- Using AI for simple deterministic rules.
- Automating sensitive decisions without review.
- Sending data without reviewing permission and retention.
- Measuring only whether an answer sounds convincing.
Execution
How I work
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I define acceptable error and who makes the final decision.
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I prepare real examples and a small evaluation.
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I build the workflow with controls and structured output.
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I measure quality, cost and escalation cases.
Useful context
Related reading
Frequently asked questions
Does human review need to disappear?
No. Often the value is in preparing and prioritizing while a person retains sensitive decisions.
How is quality measured?
With representative examples, quality criteria, execution cost and tracking of corrected or rejected cases.
Can sensitive data be protected?
The design should review what is sent, to which provider, for how long, and whether it can be minimized or anonymized.
Next step
Review a workflow before adding AI
A few real examples are usually enough to decide between AI, rules, an integration or a combination.