Bruviti
Use case

Email Case Resolution

Agent automation that auto-resolves ≥40% of routine customer emails with policy-compliant decisions and full system updates, achieving median response time under 2 minutes with 24/7 coverage.

Challenge

Call centers receive thousands of routine emails about parts pricing, availability, RMAs, and order tracking. Agents jump between Outlook, legacy desktop ERP, web order portals, and carrier sites. Each inquiry takes 15–20 minutes, varies by agent, and stops after business hours—driving backlogs, cost, and lower CSAT.

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The objective

auto-resolve ≥40% of routine emails with policy-compliant decisions and full system updates; achieve median response <2 minutes; enable 24/7 global coverage.

Solution: How the AI layer changed the operating model

Learning and setup

Powered by Bruviti’s AI Operating Layer, the agent solution applied its Vision, NLP, Policy, and Predictive models. Training data came from historical emails and agent responses, SOPs and runbooks, screen recordings and annotated screenshots. This enabled the AI agent to recognize and interpret email subject and body, attachments PDF PNG, part numbers and SKUs, order IDs, RMA IDs, serial numbers, customer and account IDs, warranty codes, tracking numbers, carrier names, and priority flags.

AI Models and Training Data workflow diagram AI MODELS TRAINING DATA Vision Model NLP Model Policy Model Predictive Model Historical Emails SOP Runbooks Screen Recordings AI layer

Workflow orchestration

The Bruviti AI Agent running on desktop visually reads incoming emails from desktop email client, classifies intent, extracts key information, and chooses the correct path: price and availability, order status, RMA create or status, or shipment tracking. It further navigates CRM, ERP, Parts Portal, RMA portals, and carrier sites, using APIs where available and Vision-based automation for legacy desktop or web flows. Logic branches on policy—for example, expired warranty or account credit hold—while enforcing entitlement and audit requirements.

Email Agent Solution Architecture diagram Email Agent Mail Service CRM System ERP System Order System RMA System Carrier Sites Policy Engine Analytics

Execution and resolution

The AI agent ingests and deduplicates messages, classifies intent, extracts entities, validates entitlement and policy, and executes actions: fetch price and availability, check order or RMA status, create RMA with labels, or retrieve shipment status. It drafts a brand-safe response, updates ERP CRM, sends the reply, and logs a full audit trail to analytics. Low-confidence outcomes, missing data, or mismatched records are routed to human agents with prefilled context and suggested next steps.

Workflow Execution diagram Intake Classify Extract Validate Select Path Execute Action Compose Reply Update Systems Send Reply Escalate LOW CONFIDENCE EXCEPTION

Results

≥40%Routine emails auto-resolved with full audit and policy compliance
<2 minMedian handling time and always on 24/7 coverage
300+Agent hours saved per week

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Frequently asked questions

Can AI automatically resolve customer service emails for parts orders?

Yes. AI reads incoming emails, classifies the intent such as pricing inquiry, order status, or RMA request, extracts part numbers and account details, and executes the required actions across ERP and order systems. It drafts a policy-compliant response and sends it within minutes, auto-resolving routine inquiries without human intervention.

How does email automation work for service teams?

Email automation uses AI to monitor inbound messages, identify the request type, pull data from connected systems like ERP and CRM, perform the required transaction, and generate a response. It handles routine tasks such as price checks, shipment tracking, and return authorizations around the clock, freeing agents to focus on complex issues.

What types of customer emails can be automated with AI?

AI can automate emails involving parts pricing and availability lookups, order status inquiries, shipment tracking requests, RMA creation and status checks, and routine warranty questions. These high-volume, rule-based requests typically account for 40 to 60 percent of inbound service email volume and follow predictable workflows suitable for automation.