Find dealers that merit review
Benchmark cost per unit, repair frequency, and repairs per unit. Investigate persistent deviations within comparable product and operating populations.
Warranty Decision Intelligence · For OEM leaders
AI-powered Warranty Auditing & Controls
Identify risk earlier.
Focus every audit.
Strengthen dealer accountability.
Connect dealer benchmarking, AI-powered evidence review, and claim scoring to reduce avoidable warranty cost—before payment and throughout the audit lifecycle.
Answer • Decide • ActEvidence-backed reviewHuman oversight
From broad surveillance to targeted controls
Better audit targeting. Faster evidence review.
More defensible decisions.
Put evidence in the auditor’s hands
Process repair orders, technician labor time cards, and parts inspection records to surface inconsistencies and missing support.
Reduce avoidable cost upfront
Apply evidence-based claim scoring and adjudication before payment. Use audit findings to improve future decisions and dealer guidance.
Dealer scoring & benchmarking
Compare like with like.
Then follow the evidence.
A high-cost dealer is a review candidate. It may also serve a harder-working fleet, a different product mix, or vehicles affected by a quality issue.
| Benchmark signal | What the auditor should investigate | How Circuitry.ai supports the decision |
|---|---|---|
| Cost per unit Warranty expense ÷ eligible units | Product mix, age, utilization, repair severity, and local labor rates | Identify cost outliers within defined peer cohorts and drill into contributing claims. |
| Repair frequency Repair events per exposure period | Repeat visits, repair effectiveness, campaigns, and emerging field issues | Surface recurring repair patterns for dealer review or quality escalation. |
| Repairs per unit Repair events ÷ unique serviced units | Multiple operations, repeat component replacement, and concentrated VIN histories | Connect claim histories to identify clusters requiring evidence review. |
| Labor & parts patterns | Extra labor, overlapping time, duplicate operations, unusual part quantities | Compare claimed work with repair narratives, time cards, and inspection evidence. |
| Evidence & policy exceptions | Missing support, inconsistent identifiers, approvals, and return obligations | Score evidence completeness and highlight policy questions for an auditor. |
Design the score around your network. Agree denominators, repair-event definitions, peer groups, minimum sample sizes, observation windows, and policy versions. Show score drivers and data gaps. Route low-volume or uncertain results to review.
Methodology above is a recommended configuration approach, not a claim that every OEM uses the same criteria or thresholds. Repair frequency and repairs per unit must be defined separately to avoid double counting.
AI-powered audit assistance
Turn scattered documents into
a focused evidence review.
Repair orders
Extract complaint, cause, correction, identifiers, dates, mileage, parts, and labor operations. Compare the repair story with the claim and applicable policy.
Technician labor time cards
Reconcile technician, job, and timestamps. Flag overlaps, missing punches, and unexplained differences between claimed and documented work.
Parts inspection
Bring inspection reports, return records, and available photos into the review. Check consistency with the claimed failure and required return process.
Evidence sets the scope.
A dealer score prioritizes review. Claim scoring identifies potentially unsupported amounts. The auditor confirms the finding, policy basis, and supported debit scope.
Document extraction and image analysis support auditor judgment. AI cannot establish a physical part’s condition without suitable inspection evidence; missing information is a request for review, not proof of misconduct.
Connected audit journey
From dealer signal to corrective action.
Keep the trail from benchmark to evidence, finding, dealer response, and final disposition.
Target & scope
Rank dealer signals, validate cohorts, and select a risk-based claim sample plus a random control sample.
Review & substantiate
Process records, reconcile evidence, and document claim-level policy exceptions.
Respond & decide
Request clarification, capture dealer responses, and route findings for authorized approval.
Correct & monitor
Track final outcomes, deliver coaching, and feed validated findings into upstream controls.
Recommended operating model. Configure systems, approval routes, notices, and deadlines for your OEM’s policies and applicable jurisdiction.
Controls that preserve dealer trust
Target the issue. Support the relationship.
Explain why a claim is questioned
Use specific evidence and policy references. Distinguish a documentation gap from unnecessary repair, overbilling, or suspected fraud.
Give dealers a path to resolve it
Request missing evidence and support self-review or coaching where appropriate. Retain the dealer’s response with the final decision.
Approve proportionate action
Limit proposed chargebacks to supported findings. Apply authorized review, current requirements, and appeal processes before financial action.
Configure controls for relevant contracts and current local rules; audit windows, notice, appeal rights, and chargeback restrictions vary. Do not extrapolate a targeted sample to the full population without an approved, applicable methodology.
Research-informed approach
What public OEM examples teach us.
| Public example | Documented approach | Decision intelligence opportunity |
|---|---|---|
| Ford Historical policy record [1] | Staged counseling, self-review, warranty review, and audit follow-up; abnormal performance can prompt escalation. | Monitor persistent deviations and prioritize evidence review before escalation. |
| Ford California board record, 2025 [2] | Allegations can trigger an investigation and warranty study before a formal audit. | Keep allegations distinct from validated findings and connect investigation evidence. |
| General Motors Historical court findings, 2004 [3] | Elevated warranty expense prompted dealer self-reviews before an expense review; labor and parts-retention issues featured in findings. | Connect cost signals with time records, parts records, and dealer correction. |
| Cross-brand audit practice Independent specialist [4] | GM expense, Chrysler POPPS, and Ford 126 reports inform high-expense RO selection; random RO review is another option. | Combine targeted review with a control sample to evaluate missed risk. |
Examples establish public precedents, not current OEM audit playbooks. Detailed current selection formulas and thresholds are often not public. No OEM endorsement is implied.
Measure outcomes, not just findings
Make the value visible.
Better audit targeting
Track substantiated-finding rate, false positives, risk coverage, and missed issues in random samples.
Auditor productivity
Measure document preparation time, claims reviewed per auditor, evidence completeness, and audit cycle time.
Lower avoidable claim cost
Separate pre-payment corrections, proposed debits, approved chargebacks, collected amounts, and appeal reversals.
Set a baseline and matched comparison. Avoid double counting prevented cost and later recovery. Benefits depend on scope, evidence availability, and adoption.
See your audit opportunity
Make every warranty
review count.
Request a demo of Circuitry.ai’s Warranty Decision Intelligence.
Explore your dealer benchmarking, repair-order evidence, technician time records, parts inspection, and pre-payment controls.
- Select a dealer cohort or repair category.
- Review score drivers and claim evidence.
- Define an audit productivity and cost baseline.
Request your demo
Questions from OEM teams
Designed for your warranty operation.
Does a high dealer score automatically trigger a chargeback?
No. It prioritizes review. Claim-level evidence, applicable policy, dealer response, and authorized approval determine the final action.
Can this work alongside our existing claims system?
Circuitry.ai supports integration with existing warranty systems. Scope data access, document sources, scoring outputs, and decision writeback during implementation.
How do we start?
Start with one region, dealer cohort, product line, or repair category. Agree peer groups and policy rules, validate findings with auditors, and measure against a baseline before expanding.
Research & references
- Ford policy material in NHTSA investigation record — historical warranty performance measurement and audit process.
- California New Motor Vehicle Board: PR-2826-23 — Ford allegation investigation and warranty study testimony.
- General Motors Corp. v. Darling’s (2004) — historical monitoring, self-review, and audit findings.
- jlwarranty: Repair Order Review — independent cross-brand practice, not an OEM policy source.