A marketing services company adjudicates advertising claims for enterprise clients, validating that dealer promotions, digital campaigns, and marketing activities comply with brand guidelines before approving rebates and co-op funding.
Every claim meant a human reviewer reading brand guidelines, interpreting compliance rules, and cross-checking multi-document packages: invoices, scripts, screenshots, videos. Each claim took 15–25 minutes. At 60,000 claims in co-op and millions in consumer rebates a year, that was the bottleneck consuming the entire adjudication team.
RapidCanvas builds an AI system that ingests brand guidelines, extracts claim data, scores each submission, and auto-approves the straightforward ones. Adjudicators now handle exceptions instead of routine validation.
The company
This company processes marketing rebates and co-op advertising claims for a portfolio of enterprise clients across multiple regions. Its core function: review dealer claim packages, validate compliance with brand guidelines, and approve or decline funding, at scale, consistently, and with an audit trail.
In this business, accuracy and consistency aren't just operational preferences. They're the foundation of partner trust. A dealer approved on one claim and rejected on an identical claim elsewhere creates friction. Delays in processing delay dealer reimbursement. Compliance gaps create audit risk for clients.
The problem
Manual guideline interpretation at scale. Each brand had pages of guidelines with nuanced compliance rules, and adjudicators had to read, interpret, and apply them to every single claim. One client was sending multiple guideline-update emails a day, with no version control in place.
Inconsistent decisions. Different reviewers interpreted the same guidelines differently. A claim approved by one adjudicator might be declined by another, even when it met the same standard.
No version control. When brands updated their guidelines, nothing tracked it automatically. Reviewers didn't always use the latest version (creating compliance risk and rework) and had to navigate multiple tools to find the right guidelines for each claim.
A throughput ceiling. Output was capped at roughly 10-20 claims per reviewer per day. Scaling meant hiring proportionally more adjudicators, an expensive path that still wouldn't fix the consistency problem.
Inconsistent feedback on declines. Declined claims got manual, inconsistent explanations. Dealers didn't always understand why they were rejected, leading to resubmissions that clearer guidance would have avoided. There was no standardized rationale and no audit documentation.
Adjudicator burnout. Experienced reviewers spent their days on routine validation instead of higher-value work: complex edge cases, partner education, compliance strategy.
The solution
Guideline ingestion. The system ingests brand guidelines as documents and uses AI to extract the compliance rules, identify activity types, and build adjudication checklists. Changes are tracked automatically, so claims are always assessed against the current version.
Document extraction. It reads and extracts claim data from invoices, scripts, screenshots, and supporting documents. The MVP focused first on structured digital-advertising formats from major ad platforms: pulling spend amounts, campaign details, and dealer information.
Rule matching. Each claim's extracted data is checked against the rules pulled from that brand's guidelines, tuned to the brand's specific requirements, including regional variations in currency, language, and compliance across APAC, EMEA, and the Americas.
Traffic-light scoring. Every claim gets a confidence tier: green auto-approves high-confidence submissions, yellow flags ambiguous cases for a quick human look, and red declines with the specific guideline violations cited. Adjudicators focus on the exceptions instead of reviewing every claim by hand.
Workbench and team dashboard. A testing interface lets adjudicators compare the AI's recommendations against their own decisions side by side, while a team-lead dashboard tracks accuracy, productivity, exceptions, and patterns across the team, surfacing exactly where the AI diverges from expert judgment.
Continuous learning. When an adjudicator overrides a decision, the system captures why and folds it back in, improving accuracy in the next cycle.
Implementation: pilot, feedback, refine
The system was built and deployed for pilot testing with two to three experienced adjudicators handling claims for a single enterprise brand in parallel with their manual process. Weekly feedback cycles sorted each disagreement by cause: a gap in the guidelines, an AI error, or a genuinely subjective call. Separate validation sessions with brand specialists surfaced ambiguities and missing rules the AI hadn't caught. The result was iterative refinement rather than a single big-bang deployment.
Results
Pilot underway, against measured targets:
Review time: 15–25 minutes → ~5 minutes per claim.
Throughput: ~10 → 20+ claims per reviewer per day, double the capacity from the existing team, with zero new headcount.
Accuracy: 95%+ agreement with expert adjudicator decisions, validated side by side in the pilot.
Data extraction: 98%+ accuracy on invoice and claim fields.
Guideline compliance: automatic version tracking keeps every claim assessed against the current guidelines, eliminating manual change-tracking.
Dealer resubmissions: clear, consistent AI-generated explanations are expected to cut resubmission cycles by 30–50%.
Audit trail: every decision carries a generated rationale citing the specific guidelines applied, compliance-ready documentation on every claim.
The impact
The company can now process more claim volume with the team it already has. Growth no longer requires proportional hiring.
Consistency improves: the same guidelines applied uniformly across every reviewer, region, and claim type, so dealers experience consistent decisions, and that consistency builds trust. Feedback improves alongside it: dealers get standardized explanations that reference specific guideline violations instead of inconsistent manual responses.
Compliance strengthens: every decision is traceable, auditable, and documented against the guidelines that were applied, which matters for brand audits and regulatory review.
And the adjudicators shift from routine validation to the work they were hired for: complex cases, partner education, compliance strategy. When AI handles the routine decisions and explains them consistently, the operation scales without adding headcount.
Scale Claim Volume Without Scaling Headcount
If your adjudication operation is capped by reviewer throughput and consistency is slipping as volume grows, RapidCanvas can help you build an AI system that handles the routine and escalates the exceptions. Read what our clients say about us on G2.
Contact us to explore what AI-powered claim adjudication could look like for your team. Ask about our expert-led 2-Day AI Workshops designed to accelerate your AI journey.
