“Tell us again.”
Customer concerns are re-entered, shortened, or separated from the asset history.
FOR OEM WARRANTY LEADERS
Connected
Self-Correcting
Warranty Ecosystem
Connect the entire warranty lifecycle. Turn every outcome into a better next decision.
Circuitry.ai connects customer concerns, diagnostics, coverage, and claims—so dealers, OEMs, suppliers, and customers move forward with the same context.
Answer • Decide • Act.
BETTER SERVICE DECISIONS.Better service outcomes.
ONE CONNECTED CASE
Concern → Repair → Claim → Learning
A future state. Delivered in stages.
Lower warranty costsPrevent avoidable cost at its source
A stronger dealer networkFewer handoffs, disputes, and resubmissions
A better customer experienceFaster resolution. Greater confidence.
THE COST OF DISCONNECTION
Warranty costs rise
in the gaps.
Your systems hold valuable records. The friction appears between them: a symptom loses context, a diagnostic result never reaches the claim, or a quality finding never reaches the next technician.
“Where’s the evidence?”
Repair recommendations and policy decisions are made from different versions of the case.
“Please resubmit.”
Missing narratives and mismatched parts or labor trigger rework, delay, and disagreement.
“Why did it recur?”
Return inspections and engineering corrections stay downstream of the next decision.
Every disconnected handoff adds effort. Every connected decision creates an opportunity to prevent it.
THE CONNECTED FUTURE
Your systems. Shared context.
One intelligent decision layer.
Circuitry.ai connects the evidence behind the transaction to the decision that moves it forward. Existing platforms continue to manage their records and transactions.
Dealer systemsDMS · repair orders · dealer portals
Warranty & enterpriseClaims · ERP · CRM · QMS / PLM
Diagnostic toolsCodes · tests · service procedures
Connected assetsIoT · telematics, where available
↓ Evidence & operational contextAPIs · connectors · events · governed data ingestion↑ Authorized actions & status
CIRCUITRY.AI
Warranty Decision Intelligence
Service Data Fabric + Knowledge Graph
Asset / VIN · Concern · Diagnosis · Coverage · Repair · Claim · Outcome
UnderstandLanguage, documents, images
DecidePolicy, prediction, scoring
ActOrchestration, validation, escalation
AI ADVISORSGuide the right decisionAI ANALYSTSFind the next improvementAI AGENTSExecute approved work
Target architecture, configured to your environment. Available interfaces, data permissions, system capabilities, and OEM policies determine each connection and authorized action.
THE WARRANTY LIFECYCLE, CONNECTED
Better decisions at every turn.
Explore how inputs become evidence, models become decisions, and specialized AI Workers turn decisions into coordinated action.
01Customer concern↗02Diagnostics↗03Coverage↗04Repair & intake↗05Decision & payment↗06Returns & recovery↗07Quality & prevention↗
01Customer concern
Understand the concern before it becomes a claim.
THE DISCONNECT
Symptoms lose meaning as calls, emails, and dealer notes move between systems.
01 / Understand the inputs
Customer descriptions, conversations, photos, VIN or serial number, service history, and connected-equipment signals when available.
02 / Apply the right models
Language and document understanding structure symptoms, link the right asset and history, and identify missing information.
03 / Activate AI Workers
Voice Agent • Email Agent • Service AdvisorCreate a shared service context, ask focused follow-up questions, and route the case with the original evidence attached.
04 / Improve the outcome
Less repetition for customers. Better diagnostic starting points for dealers.
Track intake completeness and repeat information requests.Concern → diagnostic caseContext travels forward.
02Diagnostics
Connect the symptom to the right diagnostic path.
THE DISCONNECT
Technicians search separate tools and may replace parts without enough evidence of the cause.
01 / Understand the inputs
Diagnostic trouble codes, test results, operating conditions, service bulletins, repair history, and IoT or telematics data where accessible.
02 / Apply the right models
Knowledge retrieval and diagnostic reasoning combine approved procedures with similar repair patterns to suggest the next test and evidence needed.
03 / Activate AI Workers
Service Advisor • Parts AdvisorGuide the technician, verify the applicable procedure and part, and carry test results into the coverage and claim record.
04 / Improve the outcome
Better first-time fix. Fewer unnecessary replacements and No Trouble Found returns.
Track first-time fix, repeat repairs, and No Trouble Found rates.Diagnostic evidence → coverage and repairContext travels forward.
03Coverage
Make the coverage decision clear, early, and consistent.
THE DISCONNECT
Policy interpretation happens late, triggering disputed repairs, approval delays, and avoidable dealer calls.
01 / Understand the inputs
Warranty terms, in-service date, mileage or operating hours, asset configuration, maintenance history, campaigns, and diagnosed failure.
02 / Apply the right models
Policy-grounded reasoning and eligibility checks evaluate the relevant terms, limits, exclusions, and supporting evidence.
03 / Activate AI Workers
Warranty Advisor • Claim Scoring AgentExplain the applicable coverage with source references; request missing facts and route ambiguous cases or goodwill exceptions to an authorized reviewer.
04 / Improve the outcome
Clearer repair expectations. Fewer coverage disputes and less back-and-forth.
Track coverage turnaround, overrides, and dispute frequency.Coverage rationale → repair authorizationContext travels forward.
04Repair & intake
Make a well-documented repair a claim-ready record.
THE DISCONNECT
Dealers re-enter repair orders, while incomplete complaint, cause, and correction narratives cause claims to bounce back.
01 / Understand the inputs
Repair orders, technician notes, labor operations, parts, photos, time records, and the approved repair scope.
02 / Apply the right models
Document extraction, multimodal analysis, and consistency checks structure the evidence and compare the actual repair with the authorization.
03 / Activate AI Workers
Claim Intake Agent • Service Advisor • Parts AdvisorPrepare the claim, identify evidence gaps before submission, and preserve links to the original repair order and diagnostic history.
04 / Improve the outcome
Cleaner submissions. Less dealer administration and fewer returned claims.
Track clean-claim rate, submission time, and resubmissions.Repair record → evidence-backed claimContext travels forward.
05Decision & payment
Resolve the claim using the whole story.
THE DISCONNECT
Reviewers chase documents across systems and apply inconsistent judgments to similar claims.
01 / Understand the inputs
Claim lines, coverage rationale, diagnosis, repair evidence, labor and parts references, prior claims, and final repair order.
02 / Apply the right models
Claim scoring, anomaly detection, repair-profile comparisons, and amount validation assess risk and identify exceptions.
03 / Activate AI Workers
Claim Scoring Agent • Warranty Advisor • Warranty AgentsRecommend the decision, route exceptions, and execute approved adjudication and payment instructions through connected systems, with confirmation and an audit trail.
04 / Improve the outcome
Faster, more consistent settlement. Less leakage and fewer avoidable escalations.
Track decision quality, cycle time, automation, and payment exceptions.Decision and settlement → return and quality contextContext travels forward.
06Returns & recovery
Close the evidence gap between OEMs and suppliers.
THE DISCONNECT
Blanket return requests and incomplete failure evidence create handling costs and recovery disagreements.
01 / Understand the inputs
Failed-part identity, supplier and lot data when available, claim evidence, return policies, inspection results, and applicable recovery terms.
02 / Apply the right models
Risk prioritization and evidence matching help identify useful returns, reconcile findings, and assemble supported recovery recommendations.
03 / Activate AI Workers
Warranty Agents • AI Analysts • Warranty AdvisorIn the target connected journey, initiate approved returns, link inspection outcomes to the original case, and prepare recovery evidence for supplier review.
04 / Improve the outcome
More purposeful parts returns. Stronger recovery submissions and fewer unsupported disputes.
Track return cost, recovery yield, and supplier dispute rates.Inspection and recovery findings → quality learningContext travels forward.
07Quality & prevention
Turn the outcome of this repair into a better next decision.
THE DISCONNECT
Claim trends arrive after costs have grown, and engineering findings rarely reach the next technician in time.
01 / Understand the inputs
Claim outcomes, repeat repairs, rejected recommendations, dealer corrections, inspection results, and engineering-confirmed causes.
02 / Apply the right models
Pattern detection, cohort analysis, forecasting, and root-cause hypotheses identify emerging issues and candidate improvements.
03 / Activate AI Workers
Claims Intelligence • AI Analysts • Service AdvisorRoute emerging signals to quality and engineering; validate corrections, update approved knowledge, and measure whether the next decisions improve.
04 / Improve the outcome
Earlier intervention. Shorter detection-to-correction cycles and a stronger customer experience.
Track issue-detection lead time, recurrence, and cost per unit in service.Validated learning → the next concern, diagnosis, and claimContext travels forward.
This is the target connected journey. Scope, AI Workers, integrations, validation, and automation levels are agreed and delivered in stages.
SELF-CORRECTING, WITH OEM CONTROL
The next decision
should be better
than the last.
A paid claim is only part of the outcome. Did the repair solve the concern? Did the returned part confirm the diagnosis? Did the correction prevent recurrence?
In the target ecosystem, Circuitry.ai brings those signals back to the point of decision. Your experts validate what changed; the approved improvement becomes available to the next technician, reviewer, or AI Worker.
Learning is governed. Feedback informs reviewed updates to knowledge, decision models, and orchestration. Policy changes and expanded decision authority remain under OEM approval.
CLOSED-LOOP IMPROVEMENT
01
Observe
Repair outcomes, overrides, returns, and recurrence
02
Explain
Find patterns and propose a correction
OEM expertise
in the loop
in the loop
04
Improve
Deploy approved updates and monitor performance
03
Validate
Expert review, historical evaluation, and release gates
Observe → Explain → Validate → Improve → Observe
ILLUSTRATIVE OEM SCENARIO
A repeat sensor replacement becomes a diagnostic improvement.
A pattern of repeat claims and No Trouble Found returns prompts investigation. Engineering confirms a connector issue. After validation, updated guidance asks technicians to check the connector before replacing the sensor. Claim review then checks for that diagnostic evidence, and analysts monitor recurrence.
FROM INTEGRATION TO INTELLIGENCE
Move beyond a development queue
for every operational improvement.
Traditional integrations can move records without preserving the reasoning behind them. A connected decision layer adds context, feedback, and configurable intelligence across the journey.
| What changes | Fragmented, rule-heavy operations | Circuitry.ai connected future state |
|---|---|---|
| Case context | Teams reconstruct the story across separate records. | Asset, concern, diagnosis, policy, and claim evidence stay linked. |
| Decision updates | New scenarios often require code changes, tickets, and release cycles. | Approved knowledge, decision configuration, and model updates adapt guidance with less custom coding. |
| Exception handling | Emails and manual queues separate the exception from its evidence. | AI Workers carry context to the right reviewer and resume authorized work after resolution. |
| Learning from outcomes | Corrections stay in notes, reports, or individual expertise. | Validated feedback improves shared guidance and decision performance. |
| Execution | People bridge gaps between approval, repair, claims, and settlement. | Orchestrated journeys coordinate approved actions and verify system responses. |
| Management insight | Reports describe costs after they occur. | AI Analysts identify emerging patterns and recommend where to intervene. |
Integration engineering, security review, data mapping, and testing still matter. AI reduces repetitive coding and operational rework as the configured ecosystem matures.
A PRACTICAL PATH TO THE FUTURE STATE
Start with one decision.
Connect the ecosystem.
Choose a product family, dealer group, or claim type with a measurable problem. Expand when the evidence supports it.
01
CONNECT & ASSISTMake expertise available.
Connect priority data and approved knowledge. Introduce Warranty and Service Advisors alongside existing work.
VALUE TO PROVELess search time and better evidence completeness.
02
VALIDATE & STANDARDIZEImprove decision quality.
Add claim intake, evidence validation, and scoring. Compare AI recommendations with expert decisions.
VALUE TO PROVECleaner claims, consistent decisions, and less rework.
03
ORCHESTRATE & AUTOMATEConnect approved actions.
Automate eligible paths across claims and related systems. Route exceptions with full context.
VALUE TO PROVEShorter cycle times and greater governed autonomy.
04
LEARN & EXPANDClose the improvement loop.
Connect return findings, supplier recovery, quality signals, and validated learning across more programs.
VALUE TO PROVELower recurrence, stronger recovery, and lower warranty cost.
A roadmap shaped by your data, your dealer network, and your decision priorities.
Map your first stageWHY CIRCUITRY.AI
Warranty depth.
Decision quality. Business value.
Purpose-built AI Advisors, Analysts, and Agents bring domain understanding to the decisions that shape warranty performance.
Warranty is the context.
Connect complaint, cause, and correction with coverage terms, labor operations, parts, dealer practices, return findings, and supplier evidence.
Explore Warranty Decision Intelligence ↗Earn the right to automate.
Ground decisions in approved evidence, evaluate them against expert outcomes, and expand automation within clear confidence and authority limits.
Explore Claim Scoring Agent ↗Measure what improves.
Link decision performance to avoidable claim cost, dealer effort, customer resolution, product quality, and realized supplier recovery.
Explore Claims Intelligence ↗YOUR OEM VALUE SCORECARD
Prove the value
before you scale.
Establish a baseline, compare similar product and dealer cohorts, and account for volume, mix, and seasonality.
↓Avoidable warranty costPaid cost per claim and per unit in service
↑Repair effectivenessFirst-time fix and repeat-repair rate
↓Dealer & customer effortRework, disputes, and time to resolution
↑Recovery & preventionRealized recoveries and issue-correction time
Set targets for your program. Quantify net value after platform, integration, and change costs; avoid counting the same saving twice.
SEE YOUR CONNECTED FUTURE
Build a warranty
ecosystem that
gets better.
Request a demo of Circuitry.ai’s Warranty Decision Intelligence and explore a staged path from your current systems to a connected, self-correcting warranty ecosystem.
- Map a priority OEM warranty journey
- See AI Workers use evidence to decide and act
- Identify your first measurable value opportunity
Built for enterprise control
Human oversightData segregationDecision auditabilityVisit the Trust Center ↗REQUEST A PERSONALIZED DEMOLet’s connect your
Let’s connect your
warranty lifecycle.
Tell us where you want to improve.
QUESTIONS OEM LEADERS ASK
A connected future.
A controlled transition.
Does this replace our DMS or warranty claims system?
The proposed approach adds a decision layer across existing systems. Those systems retain their transactional roles. The deployment defines where Circuitry.ai reads context, recommends actions, and performs authorized updates.
Do we need IoT data to get started?
No. Start with accessible policies, repair orders, claims, and service knowledge. Diagnostic feeds and IoT or telematics can enrich decisions as access, data quality, and the use case justify them.
What does “self-correcting” mean?
It means connecting real outcomes to a governed improvement cycle. AI helps surface gaps and propose changes. Experts validate them, approved updates are released, and performance is monitored. It does not mean allowing AI to rewrite warranty policy or expand its own approval authority.
How do we manage unusual or high-risk claims?
Configure authority limits, evidence requirements, and escalation thresholds for your program. Claims outside those limits go to the appropriate expert with the available evidence and decision rationale.
Can we start with a single warranty use case?
Yes. Begin with a bounded decision or journey, establish quality and value measures, and expand into additional systems and workflow stages after the agreed gates are met.