Business problem
As email volume grew, classification depended on memory, fields stayed scattered, and handoffs were difficult to track.
CASE / MULTILINGUAL EMAIL AGENT
Multilingual email agent: from inbox volume to a trackable next action
We connected email ingestion, classification, field extraction, summaries, human handling, and status tracking in one operational workflow.
Discuss an inquiry-agent pilot
WHAT YOU GET
Start with a real workflow and a measurable pilot.
Scope
Email ingestion, intent classification, structured fields, bilingual summaries, human review, and status management.
Safety boundary
High-risk messages go to a person, outbound sending requires approval, and unauthorized customer data is never used for public-model training.
WHO THIS FITS
A useful engagement starts with the right problem.
Business problem
Mixed email types, scattered fields, repeated reading, and manual transfer consumed time.
Constraints
Customer communication and business data required human handling, permissions, and logs.
Goal
Make every actionable email produce structured information, an owner, and a next step.
THE CURRENT GAP
What has to change before technology creates value.
Approach
Use anonymized history to define classification, fields, risk, and response rules.
Delivery
Email ingestion, classification, summary, extraction, human queue, and status workflow.
Evidence
Screens come from a working interface; identity, message content, and fields are anonymized.
DELIVERY PROCESS
A controlled path from evidence to a running system.
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01 / Analyze samples
Confirm email types, languages, fields, and exceptions.
Taxonomy and field standard -
02 / Design the workflow
Define what the agent and people own.
Permission and approval flow -
03 / Implement the system
Connect mailbox, model, data store, and operating interface.
Operational workspace -
04 / Improve from feedback
Track misclassification, missing fields, and human edits.
Issue and improvement log
ACCEPTANCE
Define success before expanding scope.
Classification and fields
Evaluate business category and field completeness on historical samples.
Human control
High-risk messages enter review and outbound sending requires approval.
Trackable state
Every task retains owner, time, and handling outcome.
BOUNDARIES
What this engagement does and does not promise.
Privacy
The public case does not show real identity, message body, or sensitive data.
No invented metrics
Accuracy or time-saving percentages are not claimed without a consistent test.
Related service
DEW Inquiry Agent, business automation, and operational workspaces.
FAQ
Questions teams ask before starting.
Why is no accuracy percentage published?
Email types, languages, and field definitions differ. A real project reports against an agreed test set.
Can a pilot build from this pattern?
Yes. Start with anonymized historical messages and one controlled mailbox workflow.