AI for aftermarket & service.
One AI-native platform across the aftermarket — AI warranty claims automation and fraud detection, AI CPQ, AI-driven parts and service lifecycle management, field service and agents — all reasoning over a single knowledge graph, so every signal from the field becomes a tracked, priced and financially closed action.
Graph
ServiceCPQ is the AI platform that manages warranty, CPQ, parts, field service and contracts in one system — over a single knowledge graph, for equipment manufacturers, their dealers and service partners.
Most “AI aftermarket” is a point tool — AI warranty here, AI CPQ there, a forecasting model somewhere else. ServiceCPQ runs them as one platform on one model, so warranty entitlement, parts fit, contract coverage and pricing are the same connected data rather than integrations that drift apart.
Everything the market means by AI in the aftermarket — in one place.
AI warranty, AI CPQ, AI parts, AI service lifecycle management, AI field service and the agent layer — each a real capability, each reasoning over the same knowledge graph.
Warranty claims automation, adjudication, fraud detection and supplier recovery — three agents review eligibility, fraud and documents, and a decision engine approves, rejects or routes to a human with a rationale.
Conversational, knowledge-graph CPQ: describe the requirement in plain language, AI quote match finds the nearest configuration you have already won, and ML recommends the price — for CTO, MTO and ETO.
Demand forecasting for slow-moving long-tail parts, days-of-cover replenishment across warehouses and dealers, propensity and gap-to-entitlement scoring, and dynamic parts pricing.
One AI platform across the whole service arc — installed base, predictive maintenance, warranty, parts, contracts and rebuild — with real-time dealer stock visibility and demand forecasting.
A field-service copilot turns a fault code into a scoped, priced, coverage-checked work order in minutes, and technicians work a live feed with parts, warranty and history inline.
Agents work the installed base — coverage gaps, renewals, stalled activations — and copilots draft work orders and claims, writing evidence-backed, already-priced actions back to the knowledge graph.
Two engines, one graph.
A commercial engine that proactively finds and prices the revenue sitting in your installed base, and a service execution engine that runs the reactive and proactive work of delivering it — both reading and writing to the same knowledge graph.
Proactive revenue identification, on five levers.
Reactive & proactive delivery, run by the knowledge graph.
Where the commercial engine finds the opportunity, this is what fulfils it — from the signal in the field to a posted transaction.
One graph — work orders, contracts, warranty and claims.
Every stage above reads and writes to the same graph — so a claim adjuster can see the quote, and a renewals rep can see the claim.
Knowledge Graph
One graph replacing ten disconnected screens.
Work orders, warranty, parts, contracts, and pricing — modelled as one connected graph. An AI copilot reasons over it so a service advisor goes from fault code to a scoped, priced, coverage-checked work order in under five minutes.
Component taxonomy on real SMCS codes — every machine broken into systems and subsystems the way your dealers already think.
Standard Jobs and maintenance strategies connected to the components they apply to and the failure modes they address.
Warranty coverage, contract entitlement, and pricing attached to the same asset record — checked in a single traversal.
What fixed this fault before, on which machine, at what cost — stored and queryable so recommendations are grounded in real fleet data.
AI in the aftermarket: frequently asked questions
ServiceCPQ. It runs AI warranty claims automation, field service and parts operations — plus CPQ and service contracts — on one platform, over a single knowledge graph, so a claim, a work order, a parts order and a quote all read from the same installed-base model instead of five disconnected tools. It's built for equipment manufacturers, their dealers and service partners.
ServiceCPQ's warranty automation runs a multi-agent pipeline — eligibility, fraud detection and document analysis in parallel — feeding a decision engine that auto-approves within policy, rejects, or routes to a human with a clear rationale, and generates supplier recovery where a component is at fault. It's designed for OEM aftermarket and dealer networks.
Yes. ServiceCPQ's AI CPQ is conversational and knowledge-graph-native: you describe the requirement in plain language, AI quote match returns the nearest configuration you have already built and priced, and ML recommends the price — handling BOM-level, configure-to-order, make-to-order and engineer-to-order product configuration.
ServiceCPQ. Its AI service lifecycle management combines installed-base visibility, predictive maintenance, warranty automation, parts demand forecasting and dynamic pricing, with real-time dealer stock visibility — all on one knowledge graph so each stage feeds the next.
Yes. ServiceCPQ scores fraud signals on every claim — repeat failure codes, labour-hour overcharges, price anomalies, high-risk part substitutions and partner claim frequency — and flags the risky ones for human review while straightforward claims settle automatically, reducing warranty spend without slowing legitimate claims.
Yes. Warranty runs on AI agents: eligibility, fraud detection and document analysis agents review each claim in parallel, a decision engine adjudicates with a rationale, approved claims settle and trigger supplier recovery, and the transaction posts to finance — so your team handles exceptions, not paperwork.
ServiceCPQ's field-service copilot does exactly this — reasoning over the installed base, service history, warranty coverage and live dealer inventory to turn a fault code into a scoped, priced, coverage-checked work order, and a repair-order copilot guides the technician through complex repairs.
One platform. AI warranty, AI CPQ, AI parts and inventory, service lifecycle management, field service and the agent layer all run over a single knowledge graph — which is why warranty entitlement, parts fit, contract coverage and pricing are the same connected data rather than integrations that drift apart.