Bruviti
Use case

Warranty Claims Coding

Automated warranty claims coding with NLP classification achieving 75-85% auto-coding rate and 95% consistency, reducing processing time by 90%.

Challenge

Warranty teams receive high volumes of free text claims from portals, emails, and service notes. Analysts switch between CRM and ERP to interpret issues and select codes, taking 8–12 minutes per claim. Coding varies by analyst and region, causing rework and audit gaps. Leaders lack consistent trend data to spot product issues, supplier defects, and emerging field problems.

+
The objective

auto-code ≥75% of claims across symptom, cause, part, resolution, and severity with ≥95% consistency; cut handling time to <1 minute per claim; deliver same day dashboards and alerts.

Solution: How the AI layer changed the operating model

Learning and setup

Powered by Bruviti’s AI Operating Layer, the agent solution applied its predictive multi label NLP classification, policy validation, and LLM based NLU with ontology mapping models. Training data came from historical claims and final code sets, coding guidelines and policy rulebooks, parts catalogs and product taxonomy, CRM ERP tickets, call transcripts and notes, and SME annotated samples. This enabled the AI agent to recognize free text descriptions, model and serial, product line, install and service dates, warranty tier, part IDs and SKUs, failure codes, symptom and resolution terms, region and customer, and attachments as needed.

Warranty Claims Coding Training Data and Models workflow diagram DATA Claims History Policy Guide Parts Catalog CRM ERP Tickets SME Samples Ontology Graph Data Prep Model Training Evaluation Agent Skills

Workflow orchestration

The AI agent reads each new claim event, extracts identifiers and key terms, and selects the correct path: verify entitlement, assign multi label codes, de duplicate entries, or open analyst review. It navigates CRM cases, ERP warranty modules, and the ontology graph in the same sequence a claims analyst would follow. Orchestration branches when confidence drops below a threshold or when policy rules fail, while logging each decision for audit and compliance.

Warranty Claims Coding Solution architecture diagram Claim Event Claim Coding Agent Context Fetch Ontology Graph Code Service Policy Check ERP Warranty CRM Case BI Dashboards Review Queue

Execution and resolution

The AI agent parses and normalizes text, extracts entities, maps terms to the ontology, classifies across symptom, cause, part, resolution, and severity, applies policy checks and de duplication, assigns codes with confidence, updates ERP warranty records and CRM cases, and posts explanations and rationales. Responses complete in seconds, and the system streams metrics to dashboards while batch jobs backfill historical claims. Exceptions such as missing model or serial, conflicting part numbers, or very low confidence are routed to analysts with full context attached.

Warranty Claims Coding Workflow execution diagram Ingest Claim Parse Text Extract Entities Map To Ontology Classify Labels Score Confidence Apply Rules Assign Codes Update ERP CRM Analyst Review

Results

75–85%auto coded with ≥95% consistency
<1minute per claim and ≥90% faster handling time
200–300analyst hours saved per month with 30% fewer reporting errors

Related Warranty & Returns Use Cases

Explore by Industry

Frequently asked questions

What is warranty claims coding and why is it difficult to do manually?

Warranty claims coding assigns standardized symptom, cause, part, and resolution codes to free-text warranty submissions. Manual coding is difficult because analysts must interpret inconsistent terminology, cross-reference parts catalogs and policy rules, and apply judgment across thousands of claims. This leads to variability between analysts, slow processing, and audit gaps.

How does AI improve consistency in warranty claim processing?

AI applies the same classification logic to every claim using trained NLP models and ontology mappings, eliminating analyst-to-analyst variability. It normalizes free-text descriptions, maps terms to standardized codes, and enforces policy rules uniformly. This delivers 95 percent or higher coding consistency while processing claims in under one minute each.

What are the benefits of automating warranty claims analysis?

Automated warranty claims analysis reduces processing time by up to 90 percent, improves coding accuracy and consistency, and frees analysts for exception handling. It also enables same-day trend dashboards that surface product defects, supplier quality issues, and emerging field problems faster than manual reporting, supporting proactive quality improvement decisions.