+1 800 852 9001
hello@capable.insure
Request a Demo
Case Study: Guard Home Warranty
Client: Guard Home Warranty is the home appliance insurance company working across the US
They’re at ~150 claims/day in peak seasons; ~50/day mid-season, and grow twice a year.
Goal: Faster, more accurate claim review without losing the human touch
Impact: 80% faster claim responses; 30% improvement in decision accuracy
The Challenge:
Working with hundreds of clients a day at scale requires an individual approach and strict workflow at the same time.
Claim’s manager checklist includes a huge variety of details they should check for every claim:
Client’s plan inclusions
Insurance activation
Coverage limits availability
Contract coverage exclusions (global + item-specific, for 30+ different home systems/appliances)
Home inspection findings review and juggling between hundreds of different Home Inspection
Call notes
Technician assessments
Dozens of denial qualifiers and documentation requirements
Keeping all of this “in head” is both risky and slows Claim rep work.
Claim processing becomes the biggest roadblock for the Insurance business.
- It’s the main operational proccess for every Insurance company
- As you go grow - you hire new people, which is costly and requires a lot of training
- Human factor: something could be missed during the verification process and the mistake can cost your business thousands.
We started with a deep discovery
Our first goal - investigate all possible scenarios, as it is the most crucial in every insurance business.
Now the main goal is to set up our AI agent to understand scenarios correctly and suggest the answer equally to Claim rep and even with higher accuracy rate.
We started with analyzing thousands of past claims: we had the AI agent to classify due diligence questions and denial messages, and conditions on what and why was sent.
We transform the contract terms into rules for the AI to rely on: standard exclusions, item-specific exclusions.
Cataloged 30+ qualification rules that drive approvals/denials or additional info requests
We created a complete visualization of the workflow, since it has a lot of branches, dependencies and conditions.
Our Approach to the AI Agent:
- Multi-agent Workflow.
The main goal is to be confident in AI answers' accuracy.
AI will hallucinate if we just send a customer messages with a full set of rules in one prompt to LLM.
The logic behind our AI Agent is way more bulletproof and solid.
We built a Multi-Agent Validation Pipeline (30+ Rules) where each agent validates a single rule, and returns pass/flag/deny.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Each message is contract-backed and includes a related contract section reference, which was an important client requirement to give clients transparency.
- Additional Data
For higher accuracy, we had to include other essential artifacts into analysis: Home inspection PDFs, technician notes, call transcripts, and client data from the CRM.
We connected all the sources and included them in the database and the checking pipeline.
- Native Integration into the Ticketing System (ZohoDesk)
We embedded a “Generate” button in the client’s ticketing system ZohoDesk.
- Decision context
After the response draft is generated, it followed with the Decision Context.
- LLM Speed Optimization: How Do We Achieve a 1 Second Latency.
Pre-generation on message arrival: The backend composes the draft in our database as soon as a new message hits the ticket.
So when a rep clicks “Generate,” it pulls out a ready-to-send draft in ~1s just from our database - no need to wait for a model to process 30+ checks.
- Human-in-the-Loop by Design
The AI never sends messages on its own — it only provides draft responses, which a claim rep can review, approve, and send from their own account.
Outcomes for Guard Home Warranty
Operational scale without training new staff. ~80% reduction in time to answer a client message.
~30% improvement in decisions accuracy
Decisions map to specific contract sections.
Reduced manual checks.and lower cognitive load for reps.
Customers get resolutions quicker.
Want to see it inside your ticketing system?
Capable.Insure helps carriers deploy AI claim agents - fast, accurate, reliable.
Let’s talk
+1 800 852 9001
+1 800 852 9001
42 Read Way, Suite 42v, New Castle, DE 19720
©2025 - Capable Insure
Your information (name and email address) will only be used to contact you. We will not share it with third parties.
+1 800 852 9001
hello@capable.insure
Request a Demo
Case Study: Guard Home Warranty
Client: Guard Home Warranty is the home appliance insurance company working across the US
They’re at ~150 claims/day in peak seasons; ~50/day mid-season, and grow twice a year.
Goal: Faster, more accurate claim review without losing the human touch
Impact: 80% faster claim responses; 30% improvement in decision accuracy
The Challenge:
Working with hundreds of clients a day at scale requires an individual approach and strict workflow at the same time.
Claim’s manager checklist includes a huge variety of details they should check for every claim:
Client’s plan inclusions
Insurance activation
Coverage limits availability
Contract coverage exclusions (global + item-specific, for 30+ different home systems/appliances)
Home inspection findings review and juggling between hundreds of different Home Inspection
Call notes
Technician assessments
Dozens of denial qualifiers and documentation requirements
Keeping all of this “in head” is both risky and slows Claim rep work.
Claim processing becomes the biggest roadblock for the Insurance business.
- It’s the main operational proccess for every Insurance company
- As you go grow - you hire new people, which is costly and requires a lot of training
- Human factor: something could be missed during the verification process and the mistake can cost your business thousands.
We started with a deep discovery
Our first goal - investigate all possible scenarios, as it is the most crucial in every insurance business.
Now the main goal is to set up our AI agent to understand scenarios correctly and suggest the answer equally to Claim rep and even with higher accuracy rate.
We started with analyzing thousands of past claims: we had the AI agent to classify due diligence questions and denial messages, and conditions on what and why was sent.
We transform the contract terms into rules for the AI to rely on: standard exclusions, item-specific exclusions.
Cataloged 30+ qualification rules that drive approvals/denials or additional info requests
We created a complete visualization of the workflow, since it has a lot of branches, dependencies and conditions.
Our Approach to the AI Agent:
- Multi-agent Workflow.
The main goal is to be confident in AI answers' accuracy.
AI will hallucinate if we just send a customer messages with a full set of rules in one prompt to LLM.
The logic behind our AI Agent is way more bulletproof and solid.
We built a Multi-Agent Validation Pipeline (30+ Rules) where each agent validates a single rule, and returns pass/flag/deny.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Each message is contract-backed and includes a related contract section reference, which was an important client requirement to give clients transparency.
- Additional Data
For higher accuracy, we had to include other essential artifacts into analysis: Home inspection PDFs, technician notes, call transcripts, and client data from the CRM.
We connected all the sources and included them in the database and the checking pipeline.
- Native Integration into the Ticketing System (ZohoDesk)
We embedded a “Generate” button in the client’s ticketing system ZohoDesk.
- Decision context
After the response draft is generated, it followed with the Decision Context.
- LLM Speed Optimization: How Do We Achieve a 1 Second Latency.
Pre-generation on message arrival: The backend composes the draft in our database as soon as a new message hits the ticket.
So when a rep clicks “Generate,” it pulls out a ready-to-send draft in ~1s just from our database - no need to wait for a model to process 30+ checks.
- Human-in-the-Loop by Design
The AI never sends messages on its own — it only provides draft responses, which a claim rep can review, approve, and send from their own account.
Outcomes for Guard Home Warranty
Operational scale without training new staff. ~80% reduction in time to answer a client message.
~30% improvement in decisions accuracy
Decisions map to specific contract sections.
Reduced manual checks.and lower cognitive load for reps.
Customers get resolutions quicker.
Want to see it inside your ticketing system?
Capable.Insure helps carriers deploy AI claim agents - fast, accurate, reliable.
Let’s talk
Call:
+1 800 852 9001
Email:
+1 800 852 9001
Address:
42 Read Way, Suite 42v, New Castle, DE 19720
©2025 - Capable Insure
Your information (name and email address) will only be used to contact you. We will not share it with third parties.
+1 800 852 9001
hello@capable.insure
Solution
How we work
Case Study
Request a Demo
Case Study: Guard Home Warranty
Client: Guard Home Warranty is the home appliance insurance company working across the US
They’re at ~150 claims/day in peak seasons; ~50/day mid-season, and grow twice a year.
Goal: Faster, more accurate claim review without losing the human touch
Impact: 80% faster claim responses; 30% improvement in decision accuracy
The Challenge:
Working with hundreds of clients a day at scale requires an individual approach and strict workflow at the same time.
Claim’s manager checklist includes a huge variety of details they should check for every claim:
Client’s plan inclusions
Insurance activation
Coverage limits availability
Contract coverage exclusions (global + item-specific, for 30+ different home systems/appliances)
Home inspection findings review and juggling between hundreds of different Home Inspection
Call notes
Technician assessments
Dozens of denial qualifiers and documentation requirements
Keeping all of this “in head” is both risky and slows Claim rep work.
Claim processing becomes the biggest roadblock for the Insurance business.
- It’s the main operational proccess for every Insurance company
- As you go grow - you hire new people, which is costly and requires a lot of training
- Human factor: something could be missed during the verification process and the mistake can cost your business thousands.
We started with a deep discovery
Our first goal - investigate all possible scenarios, as it is the most crucial in every insurance business.
Now the main goal is to set up our AI agent to understand scenarios correctly and suggest the answer equally to Claim rep and even with higher accuracy rate.
We started with analyzing thousands of past claims: we had the AI agent to classify due diligence questions and denial messages, and conditions on what and why was sent.
We transform the contract terms into rules for the AI to rely on: standard exclusions, item-specific exclusions.
Cataloged 30+ qualification rules that drive approvals/denials or additional info requests
We created a complete visualization of the workflow, since it has a lot of branches, dependencies and conditions.
Our Approach to the AI Agent:
- Multi-agent Workflow.
The main goal is to be confident in AI answers' accuracy.
AI will hallucinate if we just send a customer messages with a full set of rules in one prompt to LLM.
The logic behind our AI Agent is way more bulletproof and solid.
We built a Multi-Agent Validation Pipeline (30+ Rules) where each agent validates a single rule, and returns pass/flag/deny.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Each message is contract-backed and includes a related contract section reference, which was an important client requirement to give clients transparency.
- Additional Data
For higher accuracy, we had to include other essential artifacts into analysis: Home inspection PDFs, technician notes, call transcripts, and client data from the CRM.
We connected all the sources and included them in the database and the checking pipeline.
- Native Integration into the Ticketing System (ZohoDesk)
We embedded a “Generate” button in the client’s ticketing system ZohoDesk.
- Decision context
After the response draft is generated, it followed with the Decision Context.
- LLM Speed Optimization: How Do We Achieve a 1 Second Latency.
Pre-generation on message arrival: The backend composes the draft in our database as soon as a new message hits the ticket.
So when a rep clicks “Generate,” it pulls out a ready-to-send draft in ~1s just from our database - no need to wait for a model to process 30+ checks.
- Human-in-the-Loop by Design
The AI never sends messages on its own — it only provides draft responses, which a claim rep can review, approve, and send from their own account.
Outcomes for Guard Home Warranty
Operational scale without training new staff. ~80% reduction in time to answer a client message.
~30% improvement in decisions accuracy
Decisions map to specific contract sections.
Reduced manual checks.and lower cognitive load for reps.
Customers get resolutions quicker.
Want to see it inside your ticketing system?
Capable.Insure helps carriers deploy AI claim agents - fast, accurate, reliable.
Let’s talk
Call:
+1 800 852 9001
Email:
+1 800 852 9001
Address:
42 Read Way, Suite 42v, New Castle, DE 19720
©2025 - Capable Insure
Your information (name and email address) will only be used to contact you. We will not share it with third parties.
+1 800 852 9001
hello@capable.insure
Solution
How we work
Case Study
Request a Demo
Case Study: Guard Home Warranty
Client: Guard Home Warranty is the home appliance insurance company working across the US
They’re at ~150 claims/day in peak seasons; ~50/day mid-season, and grow twice a year.
Goal: Faster, more accurate claim review without losing the human touch
Impact: 80% faster claim responses; 30% improvement in decision accuracy
The Challenge:
Working with hundreds of clients a day at scale requires an individual approach and strict workflow at the same time.
Claim’s manager checklist includes a huge variety of details they should check for every claim:
Client’s plan inclusions
Insurance activation
Coverage limits availability
Contract coverage exclusions (global + item-specific, for 30+ different home systems/appliances)
Home inspection findings review and juggling between hundreds of different Home Inspection
Call notes
Technician assessments
Dozens of denial qualifiers and documentation requirements
Keeping all of this “in head” is both risky and slows Claim rep work.
Claim processing becomes the biggest roadblock for the Insurance business.
- It’s the main operational proccess for every Insurance company
- As you go grow - you hire new people, which is costly and requires a lot of training
- Human factor: something could be missed during the verification process and the mistake can cost your business thousands.
We started with a deep discovery
Our first goal - investigate all possible scenarios, as it is the most crucial in every insurance business.
Now the main goal is to set up our AI agent to understand scenarios correctly and suggest the answer equally to Claim rep and even with higher accuracy rate.
We started with analyzing thousands of past claims: we had the AI agent to classify due diligence questions and denial messages, and conditions on what and why was sent.
We transform the contract terms into rules for the AI to rely on: standard exclusions, item-specific exclusions.
Cataloged 30+ qualification rules that drive approvals/denials or additional info requests
We created a complete visualization of the workflow, since it has a lot of branches, dependencies and conditions.
Our Approach to the AI Agent:
- Multi-agent Workflow.
The main goal is to be confident in AI answers' accuracy.
AI will hallucinate if we just send a customer messages with a full set of rules in one prompt to LLM.
The logic behind our AI Agent is way more bulletproof and solid.
We built a Multi-Agent Validation Pipeline (30+ Rules) where each agent validates a single rule, and returns pass/flag/deny.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Output moves to the next agent only if the current rule passes - creating an accurate chain of checks.
Each message is contract-backed and includes a related contract section reference, which was an important client requirement to give clients transparency.
- Additional Data
For higher accuracy, we had to include other essential artifacts into analysis: Home inspection PDFs, technician notes, call transcripts, and client data from the CRM.
We connected all the sources and included them in the database and the checking pipeline.
- Native Integration into the Ticketing System (ZohoDesk)
We embedded a “Generate” button in the client’s ticketing system ZohoDesk.
- Decision context
After the response draft is generated, it followed with the Decision Context.
- LLM Speed Optimization: How Do We Achieve a 1 Second Latency.
Pre-generation on message arrival: The backend composes the draft in our database as soon as a new message hits the ticket.
So when a rep clicks “Generate,” it pulls out a ready-to-send draft in ~1s just from our database - no need to wait for a model to process 30+ checks.
- Human-in-the-Loop by Design
The AI never sends messages on its own — it only provides draft responses, which a claim rep can review, approve, and send from their own account.
Outcomes for Guard Home Warranty
Operational scale without training new staff. ~80% reduction in time to answer a client message.
~30% improvement in decisions accuracy
Decisions map to specific contract sections.
Reduced manual checks.and lower cognitive load for reps.
Customers get resolutions quicker.
Want to see it inside your ticketing system?
Capable.Insure helps carriers deploy AI claim agents - fast, accurate, reliable.
Let’s talk
Call:
+1 800 852 9001
Email:
+1 800 852 9001
Address:
42 Read Way, Suite 42v, New Castle, DE 19720
©2025 - Capable Insure
Your information (name and email address) will only be used to contact you. We will not share it with third parties.