AI Skills

Detect Claim Anomaly

Warranty claims volumes can make it difficult to catch irregularities through manual review alone. The Detect Claim Anomaly skill brings AI-powered inspection into the warranty claims workflow, invoking the Anomaly Detection Rule store app to flag claims that require further investigation before adjudication.

The skill analyzes incoming claims against historical data, supporting documentation, and images to surface anomalies across six detection categories:

Duplicate claim detection — Identifies claims that have already been submitted for the same repair or service event, preventing duplicate payouts.
Parts mismatch detection — Flags claims where listed parts do not match the supporting documentation or attached images, catching transcription errors or inflated claims.
Image reuse detection — Detects claims that include the same photograph or image submitted with a prior claim, a common indicator of fraudulent or copy-paste submissions.
Dealer pattern detection — Identifies clusters of similar claims submitted by the same dealer within a specified period, surfacing unusual submission behavior.
Cross-asset customer claim detection — Flags similar claims submitted for the same customer across multiple assets, highlighting potential systemic issues or coordinated claim activity.
Product usage mismatch detection — Compares product usage data on the current claim against previous claim history for the same asset, catching inconsistencies in reported mileage, hours, or operating conditions.

Enhance Non-Conformance Description

Unstructured or incomplete issue descriptions are a leading cause of back-and-forth clarification in quality workflows. This skill evaluates free-text issue descriptions submitted by field teams and service partners, highlights missing details, and suggests improved versions for clarity and completeness — ensuring Non-Conformances are actionable from the point of submission.

AI Agents

Create Recall Corrective Actions Agent

Recall campaigns often begin with dense technical documents — repair instructions, service bulletins, engineering notices — that must be manually reviewed and translated into structured corrective actions. This agent automates that process.

The agent analyzes uploaded documents, extracts the required corrective actions, and identifies the associated parts and labor for each action. It then creates the corrective action records and their corresponding charges directly within the recall campaign, reducing manual effort and minimizing transcription errors.

Capabilities:

Analyzes repair instructions, bulletins, and related documents to extract corrective action details
Identifies required parts and labor associated with each corrective action
Automatically creates corrective action records and corrective action charges within the campaign

Plan and Execute Recall Campaign Phases Agent

Planning a recall rollout across thousands of impacted assets — while accounting for parts constraints and servicing capacity — is one of the most complex tasks a recall manager faces. This agent turns that process from weeks of manual planning into an AI-driven recommendation.

The agent evaluates the full picture: impacted assets added to the campaign, corrective actions (whether AI-generated or manually created), calculated parts requirements based on corrective action specifications and relevant asset counts, and parts availability data uploaded by the recall manager. Using this information, the agent determines which assets can be serviced and when, then structures the campaign into phases and sub-phases accordingly.

Capabilities:

Analyzes impacted assets, corrective actions, parts requirements, and parts availability to build an informed rollout plan
Determines asset servicing readiness based on parts supply against demand
Generates campaign phases and sub-phases that align execution to real-world parts constraints

Quality: Non-Conformance Submitter Workflow

A structured, playbook-driven workflow that guides field teams and service partners step-by-step to submit complete and accurate Non-Conformances — reducing clarification cycles and improving downstream resolution speed.

Capabilities:

AI-guided submission experience — Walks users through a structured sequence starting with install base selection, automatically populating related context such as account and service organization to reduce manual entry.
Intelligent field prediction using integrated playbook agents — Extracts insights from descriptions and attachments to answer 5W2H questions and predict severity, issue type, and other structured fields automatically.
AI-powered duplicate detection — Identifies similar existing Non-Conformances before submission, allowing users to deflect duplicates and follow progress via watchlist.
Early correction and cost capture — Enables users to document implemented correction actions, attach evidence, and itemize costs through expense lines directly within the submission flow.
Resume incomplete submissions — Submissions that are not completed can be resumed directly from the standard ticket page, ensuring no work is lost.