High-volume email inboxes - customer support queues, procurement inboxes, HR inquiry mailboxes - spend significant human time doing triage: reading each email, deciding what type it is, and forwarding it to the right person or team. AI classification handles this triage layer reliably and at scale.

The Problem

Manual email triage creates bottlenecks and inconsistency. The same inquiry type may be routed differently depending on who is doing triage that day. Urgent requests sit in a queue with routine ones. Emails requiring multiple teams get forwarded once and the secondary team is forgotten.

The result is slower response times, routing errors that require re-work, and triage staff spending their time on classification rather than resolution.

The AI Approach

LLMs excel at text classification tasks, especially when categories are well-defined and the classification criteria can be expressed in natural language. Unlike traditional rule-based routing (keyword matching), LLM classification understands intent: an email that says “I need to cancel everything” is understood as a cancellation request even though it contains no category-specific keywords.

Classification is combined with extraction: the model identifies the category, urgency level, sender’s apparent intent, and any key entities (account number, product name, deadline mentioned) in a single pass.

Three-Step Build

Step 1 - Define your taxonomy. List the 5-15 categories your inbox receives. For each, write 2-3 example subjects and first sentences. This becomes the classification prompt. Clear category definitions with examples are more important than model choice for getting classification right.

Step 2 - Classify with a Bedrock call. Send the email subject and first 200 words to a fast, low-cost model (Claude Haiku or similar) with a structured classification prompt. Request a JSON response with: category, urgency (high/medium/low), extracted entities, and a one-sentence summary. The 200-word limit keeps latency and cost low; the full body is rarely needed for classification.

Step 3 - Route based on output. Use the classification JSON to trigger your routing logic: assign to a queue, create a ticket in your support system, send a Slack alert for high-urgency items, or auto-reply with an acknowledgment. Use AWS Step Functions or a simple Lambda to orchestrate the routing rules.

Where It Breaks

Ambiguous emails that could fit multiple categories are a persistent challenge. The model will pick the most likely category, but for borderline cases you want a confidence signal and a fallback to human review. Some LLMs do not natively produce well-calibrated confidence scores - use a separate verification prompt if confidence matters.

The Production Path

Start by running the classifier in shadow mode alongside your existing process: classify emails but route them manually, then compare the AI classification to what the human would have done. Measure agreement rate. Once you reach 90%+ agreement on clear cases, enable automatic routing for those categories and keep the ambiguous cases in a human review queue.