Bruviti
Use case

Knowledge-Driven Root Cause Analysis

Cut MTTR by 40-60% with AI-powered root cause analysis that auto-suggests top three root causes for 70% of incidents with 85% precision.

Challenge

SME knowledge on root causes and fixes is scattered across manuals, tickets, logs, and tribal memory. Teams repeatedly diagnose recurring issues, context switch across CRM ITSM, knowledge bases, and telemetry portals, and deliver inconsistent outcomes. MTTR stays high, knowledge attrition hurts continuity, and data only methods miss SME context that actually drives accurate diagnosis.

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The objective

cut MTTR by ≥40%. Auto-suggest a top three root cause list for ≥70% of incidents with ≥85% precision. Capture new learnings from tickets and logs and publish to the knowledge base within 24 hours.

Solution: How the AI layer changed the operating model

Learning and setup

Powered by Bruviti’s AI Operating Layer, the agent solution applied predictive, NLP LLM reasoning with retrieval, ontology graph reasoning, and policy models. Training data came from historical tickets and resolutions, service manuals and SOPs and FMEAs, field logs and telemetry, asset histories, parts consumption, SME guides, and annotated RCA sessions. This enabled the AI agent to recognize and interpret free text symptoms, failure codes, equipment model and serial, environment and duty cycle, prior fixes and parts used, log snippets and telemetry, site and region, and warranty status.

Knowledge-Driven Root Cause Analysis Training Data and Models workflow diagram SOURCES MODELS SOP Docs Tickets Logs Telemetry Asset History SME Guides NLP Reasoning Predictive Model Policy Guard Ontology Mapper Knowledge Graph

Workflow orchestration

The RCA agent listens on the event bus for new or updated cases in CRM ITSM, enriches context from Asset, Inventory, and Document modules via the ontology, then queries the knowledge graph. It determines the path to suggest likely causes, procedures, and parts, while writing status and notes back to CRM and the knowledge base and notifying stakeholders. If confidence is low or policy rules apply, the case routes to an SME with full context and a concise summary.

Knowledge-Driven Root Cause Analysis Solution architecture diagram Event Bus RCA Agent CRM System Asset Module Inventory Module Document Module Knowledge Graph Policy Engine Notification Hub Knowledge Base

Execution and resolution

The AI agent normalizes incident text, extracts entities, retrieves similar cases, ranks likely causes, and generates a stepwise troubleshooting plan. It fetches procedures and needed parts, asks clarifying questions when data gaps exist, posts recommendations to the agent desktop, and documents outcomes. Responses are completed in under two minutes, with updates posted across connected systems. Exceptions such as novel error codes, missing telemetry, or conflicting asset history are escalated to SMEs with full evidence and rationale.

Knowledge-Driven Root Cause Analysis Workflow execution diagram Intake Case Normalize Text Entity Extract Similar Cases Rank Causes TroubleshootPlan ClarifyQuestions RecommendActions Update CRM Learn andUpdate KB

Results

40–60%Reduction in MTTR
≥70%Incidents receive accurate top three cause suggestions at ≥85% precision
50%Fewer duplicate investigations and 3x reuse of proven fixes

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Frequently asked questions

How does AI-powered root cause analysis improve field service first-time fix rates?

AI-powered root cause analysis matches incoming symptoms and fault codes against a knowledge graph of historical resolutions, technical manuals, and expert diagnoses. By auto-suggesting the top three most likely causes with recommended parts and procedures, technicians arrive prepared with the right fix, improving FTFR and reducing mean time to repair by 40 to 60 percent.

What role does a knowledge graph play in equipment troubleshooting?

A knowledge graph links equipment models, failure modes, symptoms, repair procedures, and parts into a structured network that AI can traverse. When a new incident arrives, the system retrieves similar cases, ranks probable causes using graph reasoning, and surfaces proven fixes. This captures tribal knowledge that would otherwise be lost through staff turnover.

How can AI reduce repeat service visits for the same equipment issue?

AI reduces repeat visits by matching current symptoms against complete resolution histories rather than relying on individual technician memory. It identifies root causes instead of surface symptoms, recommends comprehensive fix procedures, and documents outcomes for future reuse. Organizations typically see 50 percent fewer duplicate investigations and three times greater reuse of proven solutions.