Your warranty system already works—the question is where to integrate AI without rearchitecting everything or locking into a single vendor.
Deploy warranty claims validation APIs via Python SDK integrating with your existing ERP and warranty systems. REST endpoints for entitlement verification, fraud detection models, and NFF analysis run alongside SAP or Oracle without vendor lock-in.
Industrial equipment warranty data lives in SAP tables designed 20 years ago. Equipment serial formats changed three times across acquisitions. Your AI vendor expects clean JSON.
Closed SaaS platforms force your warranty logic into their workflows. When the vendor changes pricing or deprecates features, you're rebuilding integrations from scratch.
AI flags a pump claim as fraudulent but won't explain why. Your warranty manager needs justification for customer disputes. The vendor says retrain the model for $50K.
Bruviti's Python SDK exposes warranty claim validation as REST endpoints you call from existing systems. Your SAP instance continues handling transactions—API calls add AI validation at claim submission, entitlement check, and return receipt stages.
The SDK handles three integration patterns. Synchronous validation for real-time claim submission checks, async batch processing for nightly fraud scans across historical claims, and event-driven webhooks triggered when refurbished units complete testing and need disposition codes.
When the model flags a suspicious claim, the response includes feature attribution scores explaining exactly which data points triggered the fraud flag. Your warranty analysts see why a claim failed—serial number mismatch with install base records, claim frequency anomaly, or NFF return pattern inconsistent with failure mode.
Deploy to your AWS VPC or Azure tenant. Models run on your infrastructure consuming your warranty data. Telemetry exports to your data lake. No vendor sees your claims data, no migration project when you upgrade.
API automatically classifies industrial equipment claims by failure mode and warranty coverage type, routing to correct processing queues.
Computer vision API analyzes microscope images from refurbishment centers, validating claimed defects on returned CNC components and hydraulic valves.
Your warranty systems evolved over decades through acquisitions and product line expansions. CNC equipment from the 1990s uses different serial formats than the automation systems acquired in 2015. Parts numbering schemes don't align. Installation records live in regional databases with inconsistent schemas.
Industrial equipment has 10-30 year lifecycles. Warranty claims arrive years after sale when documentation gaps appear, making fraud detection crucial but pattern recognition challenging. Return logistics span multiple refurbishment centers with varying inspection procedures and coding standards.
The API accepts JSON payloads containing claim ID, equipment serial number, failure description, claim date, and optional telemetry snapshots. You map your ERP fields to these standard parameters in your integration layer. The SDK includes transformation helpers for common SAP and Oracle schema patterns, and you can extend the mapping logic for custom fields specific to your product lines.
API responses include feature attribution scores showing which data points contributed to fraud likelihood. If a claim gets flagged, you see explanations like "serial number install date mismatch" or "claim frequency 3x above baseline for this equipment type." This transparency lets warranty analysts dispute false positives with evidence and helps you tune model thresholds without vendor involvement.
Yes. The platform supports model fine-tuning using your historical claims and refurbishment data. You provide labeled training sets identifying confirmed fraud cases and legitimate NFF returns. The SDK handles training infrastructure and version control. Your custom models remain in your deployment environment—no data leaves your network.
Your warranty business logic lives in your code calling standard REST endpoints. The API contract defines inputs and outputs but doesn't force your data into vendor-specific formats. If you switch providers or bring capabilities in-house, you reimplement the API endpoints without rewriting the integration layer. This architecture treats AI as a replaceable component, not a platform dependency.
Transfer learning addresses sparse data problems. Pre-trained models built on broad industrial equipment failure patterns provide baseline fraud detection even for older product lines with limited claims history. As your specific equipment accumulates more claims data, you incrementally fine-tune the models to capture product-specific patterns while preserving the general fraud indicators learned from the broader dataset.
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Review API documentation and SDK examples for industrial equipment warranty systems.
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