Aberrant AI

Use case

Email and Document Classification

Incoming requests are manually read, categorized, assigned, and tracked.

Why status quo fails

Manual coordination cannot create reliable control.

Incoming requests are manually read and sorted.

Important attachments are missed.

Task creation depends on inbox monitoring.

Low-confidence classifications are not separated for review.

Automation model

The workflow becomes a controlled sequence.

Classify messages, extract fields, route tasks, and flag exceptions for human review.

Message received
Content classified
Fields extracted
Task routed
Exception flagged
Owner notified

Core features

What the system needs to do.

Email classification

Document extraction

Task routing

Confidence thresholds

Review queue

Connected tools

Tools that can participate in the flow.

GmailOutlookGoogle DriveMicrosoft 365Workflow appAI model

Expected outcome

Faster triage without hiding uncertainty.

01

Implementation

Sample incoming messages

02

Implementation

Define categories and extraction fields

03

Implementation

Build review queue

04

Implementation

Tune with real examples

Next action

Automate Intake Triage

Tell us where this workflow currently breaks. We will map the automation model and the first build path.