Incoming requests are manually read and sorted.
Case study
Email and Document Classification
Incoming requests are manually read, categorized, assigned, and tracked.
Operating problem
What keeps breaking.
Important attachments are missed.
Task creation depends on inbox monitoring.
Low-confidence classifications are not separated for review.
Build model
The work becomes a clearer system.
Classify messages, extract fields, route tasks, and flag exceptions for human review.
Operating path
Messages are classified, fields are extracted, tasks are routed, and owners are notified.
AI/product layer
Separate the request, explain uncertainty, and place it in the right review queue.
Reusable improvement
Reviewed examples improve categories, extraction prompts, and routing rules.
Workflow
The sequence becomes explicit.
Message received
Content classified
Fields extracted
Task routed
Exception flagged
Owner notified
Capabilities
What the system needs.
Email classification
Document extraction
Task routing
Confidence thresholds
Review queue
Connected tools
Where the system fits.
GmailOutlookGoogle DriveMicrosoft 365Workflow appAI model
Implementation path
Faster triage without hiding uncertainty.
Step 1
Sample incoming messages
Step 2
Define categories and extraction fields
Step 3
Build review queue
Step 4
Tune with real examples
Automate Intake Triage
Tell us where this work currently breaks. We will map the first useful AI/product build path.