1 The challenge
Every claim brings a long checklist.
Working with hundreds of customers each day requires an individual approach and a consistent workflow. Claims representatives need to check plan terms, supporting evidence, and eligibility before recommending a response or approving a payout.
The objectives
- Reduce the workload of claims representatives.
- Make claims operations scalable.
- Reduce the need to hire and train additional claims representatives.
- Reduce errors in customer responses.
- Reduce inaccurate payout approvals.
The background
A representative’s checklist includes:
- Plan inclusions and activation dates
- Payout limits
- Global and item-specific exclusions for more than 30 home systems and appliances
- Home inspection findings and reports
- Call notes and customer conversations
- Technician assessments
- Denial criteria and documentation requirements
Keeping every detail in memory slows down the team and increases the risk of a missed condition. Claims operations become a bottleneck: growth requires more hiring and training, while a single verification error can cost the business thousands.
2 Our approach
Map the rules before building the agents.
Accurate coverage validation starts with a clear understanding of every coverage scenario. Discovery turned the team’s existing claims knowledge into a structured foundation for the agents.
- 01
Analyze historical claims. An AI agent classified past messages, response types, and the conditions behind each response.
- 02
Turn standard and item-specific contract exclusions into structured validation rules.
- 03
Document plan types, their differences, add-ons, and coverage limits.
- 04
Map the complete workflow, including its branches, dependencies, and decision conditions.
- 05
Catalog more than 100 validation rules for approvals, denials, and requests for additional information.
This foundation guides the agents as they validate claims, request missing information, and suggest the appropriate response.
3 The solution
AI agents inside the claims workflow.
Specialist agents check coverage, bring together supporting evidence, and prepare responses directly in the tools the claims team already uses.
Coverage rules validation
Reliable suggestions require more than a single prompt containing a claim thread. Coverage decisions depend on separate checks for plan coverage, activation dates, payout limits, exclusions, required documents, inspection findings, call context, and technician assessments.
Capable configured a multi-agent validation pipeline with more than 100 rules. Each agent checks an individual coverage rule, creating a structured review that follows the steps of a trained claims manager.
The pipeline validates the required conditions, recommends the next action, and prepares a response draft.
Calls and documents ingestion
Home inspection report PDFs, technician notes, and call transcripts were connected to the validation pipeline. The agents review this supporting evidence alongside the claim, improving the context available for each decision.
Human-in-the-loop review
The agents were embedded directly into the client’s existing claims infrastructure, Zoho Desk. Suggested responses appear inside the ticketing system, where the claims representative already works.
AI prepares the draft. The claims representative retains final control over reviewing, editing, approving, and sending it.
Decision reasoning
Each suggested response includes an explanation of the decision. For a denial, the reasoning references the relevant coverage term, allowing the claims representative to check the logic before sending the final message.
Around one second to generate a response
The validation pipeline starts processing as soon as a customer message reaches a ticket. This gives the system time to complete its analysis before the representative requests a reply.
By the time the representative clicks Generate, the suggested response appears in around one second.
A final compliance check before payout
Before a representative approves a payout, a compliance agent validates the full claim against the coverage rules and supporting evidence. It flags conflicting information for human review before payment is approved.
4 The outcomes
Less manual work.
More confident decisions.
Guard Home Warranty reported faster customer responses, more accurate decisions, and an easier path to scaling its claims operations.
Faster customer responses
Reduction in the time needed to answer a customer message.
More accurate decisions
Improvement in decision accuracy across the claims workflow.
Easier operational scalability
A structured review process supports growth with less reliance on additional manual checks.
Lower cognitive load
Representatives spend less effort keeping every rule and document detail in memory.