Hub for Warranty Claims Resolution

A warranty claim arrives as a ticket with a customer's description of a fault and very little else. Before an analyst can decide anything they have to establish which product and policy it falls under, what the warranty terms actually cover, whether this customer or this component has a claim history, and whether the described fault is one issue or three. All of that is retrieval, and all of it happens before any judgment is exercised — which is why adjudication queues are measured in days rather than minutes.

• One ticket routinely describes several distinct faults, and gets assigned as though it were one.
• Policy terms, prior claims and product history sit in three systems, so context assembly precedes every decision.
• Root-cause investigation is repeated from scratch on issues the organisation has already seen and closed.
• Where AI is used at all, the decision path is opaque — which is disqualifying for a regulated adjudication.

Hub works the claim before the analyst opens it. Incoming tickets are decomposed into their constituent issues and each issue is typed independently, so a multi-fault claim is routed as several units of work rather than one ambiguous one. The ontology then enriches every issue automatically with the supporting facts an adjudication needs — applicable policy details, warranty principles, historic claim activity on the same customer, product and component. For each issue the analyst can open an agent-run investigation into root cause, which surfaces the relevant warranty clauses and prior treatments rather than a summary. Decision logic is expressed as explicit, inspectable organisational rules; the analyst adjudicates and the system shows its work.

The hard part is not the decision, it is that the decision needs unstructured evidence to be legible first. A claim narrative is prose, warranty terms are prose, prior claim notes are prose — and the question of whether this fault matches that clause is a reading-comprehension problem, not a lookup. That is where a model earns its place. The rules that decide anything consequential stay explicit and human-authored, because an adjudication has to be defensible to a regulator, and 'the model thought so' is not a defence.

INTAKE

Multi-issue claims decomposed and typed per issue

ENRICHMENT

Ontology-driven — policy terms, product and claim history

INVESTIGATION

Agent-run root-cause analysis, per issue, on demand

DECISION LOGIC

Explicit organisational rules; inspectable, versioned

AUTONOMY

Triage and enrichment automated; adjudication stays human

1. 01Incoming claims split into their constituent issues and typed independently before assignment.
2. 02Policy details and historic claim activity attached automatically at the point of triage.
3. 03Root-cause investigation opened per issue, surfacing the warranty principles that actually apply.
4. 04Decision logic authored and versioned by the organisation, not learned implicitly by a model.
5. 05Full transparency over which rule and which evidence produced each recommendation.
6. 06Adjudication authority remains with the analyst; the system prepares, it does not decide.

Per issue

Assignment granularity, previously per ticketdesign intent

100%

Recommendations with a stated rule and evidencedesign intent

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