Bruviti
Use case

Customer Case Summarization

AI-powered case summarization delivers source-cited context in under 10 seconds, reducing handling time by 22% and improving first contact resolution by 11%.

Challenge

Agents in contact centers must piece together customer context from CRM, ticketing, email and chat, call recordings, orders, and warranty systems. This research takes 5–10 minutes per interaction, creates inconsistent answers, and drives unnecessary escalations. New hires take longer to find entitlements and prior commitments, increasing handling time and lowering first contact resolution.

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The objective

deliver a source‑cited case summary in ≤10 seconds for ≥80% of cases, cut average handling time by ≥20 percent, and improve first contact resolution by ≥10 percent while maintaining policy and privacy compliance.

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, policy, and predictive models with LLM summarization and ontology‑driven reasoning. Training data came from historical tickets and notes, email and chat transcripts, call recordings and transcripts, ERP orders and warranty records, customer profiles, knowledge base articles, SOPs, and annotated gold‑standard summaries. The AI agent learned to recognize customer and case IDs, serial and asset IDs, model details, order and shipment numbers, warranty terms, channels and timestamps, intents and sentiment, failure codes, and attachments with citations.

Customer Case Summarization Training Data and Models workflow diagram SOURCES MODELS Tickets Email Chat Call Transcripts Orders Warranty KB SOP Vision Model Policy Model LLM Gold Summaries Ontology Graph

Workflow orchestration

The AI agent reads a newly opened case or hotkey trigger, extracts identifiers, and determines the correct path: fetch CRM and ticket history, pull order and warranty status, retrieve call transcripts, and search the knowledge base. It navigates CRM cases, ERP and warranty modules, telephony, the knowledge base, and the ontology graph via APIs or Vision tools. Logic branches for policy or privacy risk, low confidence, or missing identifiers, ensuring citations and compliance at every step.

Customer Case Summarization Solution architecture diagram SYSTEMS Agent Orchestrator CRM Case Ticketing ERP Warranty Telephony Knowledge Base Ontology Graph Policy Guard Summary Service

Execution and resolution

The agent aggregates, deduplicates, and enriches records; checks entitlement and prior commitments; classifies intent; extracts key facts; and generates a five‑bullet summary with a timeline, risk flags, and citations. It recommends next best actions such as send KB link, create parts order, schedule dispatch, or escalate, then updates CRM fields and tasks, redacts PII, logs confidence, and watches for changes to refresh the summary. Exceptions like ambiguous IDs, conflicting SLAs, or missing orders route to humans with full context.

Customer Case Summarization Workflow execution diagram Case Open Gather Signals NormalizeRecords Build Timeline CheckEntitlement Generate Summary RecommendActions Post Updates Monitor Changes Human Review

Results

22%lower average handling time
85%of cases auto summarized in under 7 seconds
11%improvement in first contact resolution

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Frequently asked questions

How does AI summarize customer service cases automatically?

AI aggregates data from CRM tickets, emails, chat logs, call transcripts, and order systems, then uses natural language processing to extract key facts, timelines, and prior commitments. It generates a concise, source-cited summary in seconds, giving agents complete context without manually searching multiple systems at the start of each interaction.

What is average handling time and how can AI reduce it?

Average handling time measures the total duration an agent spends on a customer interaction, including research and after-call work. AI reduces it by instantly surfacing case history, entitlements, and recommended actions so agents skip the manual lookup phase. Organizations typically see AHT reductions of 15 to 25 percent with automated case summarization.

Why is case context important for first contact resolution in support centers?

Case context lets agents understand the full history of a customer issue, including prior interactions, warranty status, and open orders, before responding. Without it, agents ask redundant questions or miss prior commitments, leading to escalations. Instant context delivery improves first contact resolution by enabling accurate, informed responses on the initial call.