AIspresso Labs
AIspresso Labs
AIspresso Labs
How a Leading Consumer Electronics Brand Transformed Warranty Claims with Agentic AI
How a Leading Consumer Electronics Brand Transformed Warranty Claims with Agentic AI
Replacing slow, error-prone manual claims processing with an intelligent agent system — resolving 50,000+ warranty claims per year in under 24 hours instead of two weeks, with 93% fewer errors and 4x better fraud detection.
PROJECT STATUS: COMPLETED
PROJECT STATUS: COMPLETED
Results So Far
Results So Far
0%
RESOLUTION SPEED
10–14 days → < 24 hrs
0%
ACCURACY
15%+ → < 1% error
0x
FRAUD DETECTION
3% → 12% caught
0%+
CALL VOLUME
reduction in calls
About The Customer
About The Customer
A leading North American consumer electronics brand specializing in personal care appliances — hair dryers, styling tools, grooming devices, and garment care products. Annual revenue exceeding $500M, with products distributed through major retail partners and D2C channels. Processes over 50,000 warranty claims annually across multiple product lines.
A leading North American consumer electronics brand specializing in personal care appliances — hair dryers, styling tools, grooming devices, and garment care products. Annual revenue exceeding $500M, with products distributed through major retail partners and D2C channels. Processes over 50,000 warranty claims annually across multiple product lines.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
The Problem
The Problem
10–14 day resolution cycle
Claims sat in manual queues for days waiting for photo review, purchase verification, and multi-level approval routing — eroding brand trust
15%+ error rate
Different agents made different decisions on identical claims. Wrong replacements shipped, valid claims rejected, covered defects missed
Only ~3% of fraud caught
Serial returners, receipt manipulation, and cross-channel duplicate claims went largely undetected, costing hundreds of thousands annually
Zero customer visibility
No self-service tracking. Customers called back repeatedly, driving up inbound call volume and compounding agent workload
Disconnected systems
Claim data across CRM, logistics, product databases, and retail portals with no unified view — agents toggling between 4–5 systems per claim
The Solution
The Solution
An Agentic AI system on AWS Bedrock where specialized agents collaborate across the full claims lifecycle — assessing photos, detecting fraud patterns, making judgment calls on edge cases, and coordinating actions across disconnected systems.
An Agentic AI system on AWS Bedrock where specialized agents collaborate across the full claims lifecycle — assessing photos, detecting fraud patterns, making judgment calls on edge cases, and coordinating actions across disconnected systems.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
Claim Intake & Triage Agent
Ingests claims from all channels. Identifies product SKU, validates proof of purchase, classifies urgency. Straightforward claims fast-tracked for same-day resolution.
Defect Assessment Agent
Analyzes photos using computer vision against known defect patterns. Cross-references batch data, recall history, and failure-mode databases. Flags safety concerns for immediate escalation.
Fraud Detection & Resolution Agent
Cross-claim pattern analysis: serial returner detection, receipt authenticity scoring, duplicate matching. Assigns resolution with confidence scoring. Low-confidence routes to humans.
Customer Communication & Logistics Agent
Proactive status updates, resolution notifications, shipping labels, return instructions. Powers real-time self-service portal for claim tracking.
Outcomes
Outcomes
Metrics
Metrics
Before
Before
After (Agentic AI)
After (Agentic AI)
Impact
Impact
Resolution cycle
Resolution cycle
10–14 days
10–14 days
< 24 hours
< 24 hours
< 24 hours
90% faster resolution
90% faster resolution
90% faster resolution
Error / inconsistency rate
Error / inconsistency rate
15%+
15%+
< 1%
< 1%
93% fewer errors
93% fewer errors
93% fewer errors
Fraud detection
Fraud detection
~3% caught
~3% caught
~12% caught
~12% caught
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
Customer experience
Customer experience
No visibility
No visibility
Real-time self-service
Real-time self-service
60%+ reduction in calls
60%+ reduction in calls
60%+ reduction in calls
Product quality feedback loop
Defect assessment agent surfaced batch-level failure patterns feeding directly into product engineering, enabling proactive action on two product lines.
Retail partner alignment
Standardized claim processing across retail channels reduced disputes and chargebacks by 40%.
AI Accelerator
AI Accelerator
Pre-configured agent templates for warranty and returns processing, retail partner integration adapters, fraud detection model baselines, and computer vision defect assessment models. Estimated 6–8 weeks from kickoff to production.
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
Rewriting critical bottlenecks for scale and performance.
Refactor (For Constraints)
AIspresso Labs
AIspresso Labs
AIspresso Labs
How a Leading Consumer Electronics Brand Transformed Warranty Claims with Agentic AI
How a Leading Consumer Electronics Brand Transformed Warranty Claims with Agentic AI
Replacing slow, error-prone manual claims processing with an intelligent agent system — resolving 50,000+ warranty claims per year in under 24 hours instead of two weeks, with 93% fewer errors and 4x better fraud detection.
PROJECT STATUS: COMPLETED
PROJECT STATUS: COMPLETED
Results So Far
Results So Far
0%
RESOLUTION SPEED
10–14 days → < 24 hrs
0%
ACCURACY
15%+ → < 1% error
0x
FRAUD DETECTION
3% → 12% caught
0%+
CALL VOLUME
reduction in calls
About The Customer
About The Customer
A leading North American consumer electronics brand specializing in personal care appliances — hair dryers, styling tools, grooming devices, and garment care products. Annual revenue exceeding $500M, with products distributed through major retail partners and D2C channels. Processes over 50,000 warranty claims annually across multiple product lines.
A leading North American consumer electronics brand specializing in personal care appliances — hair dryers, styling tools, grooming devices, and garment care products. Annual revenue exceeding $500M, with products distributed through major retail partners and D2C channels. Processes over 50,000 warranty claims annually across multiple product lines.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
The Problem
The Problem
10–14 day resolution cycle
Claims sat in manual queues for days waiting for photo review, purchase verification, and multi-level approval routing — eroding brand trust
15%+ error rate
Different agents made different decisions on identical claims. Wrong replacements shipped, valid claims rejected, covered defects missed
Only ~3% of fraud caught
Serial returners, receipt manipulation, and cross-channel duplicate claims went largely undetected, costing hundreds of thousands annually
Zero customer visibility
No self-service tracking. Customers called back repeatedly, driving up inbound call volume and compounding agent workload
Disconnected systems
Claim data across CRM, logistics, product databases, and retail portals with no unified view — agents toggling between 4–5 systems per claim
The Solution
The Solution
An Agentic AI system on AWS Bedrock where specialized agents collaborate across the full claims lifecycle — assessing photos, detecting fraud patterns, making judgment calls on edge cases, and coordinating actions across disconnected systems.
An Agentic AI system on AWS Bedrock where specialized agents collaborate across the full claims lifecycle — assessing photos, detecting fraud patterns, making judgment calls on edge cases, and coordinating actions across disconnected systems.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
Claim Intake & Triage Agent
Ingests claims from all channels. Identifies product SKU, validates proof of purchase, classifies urgency. Straightforward claims fast-tracked for same-day resolution.
Defect Assessment Agent
Analyzes photos using computer vision against known defect patterns. Cross-references batch data, recall history, and failure-mode databases. Flags safety concerns for immediate escalation.
Fraud Detection & Resolution Agent
Cross-claim pattern analysis: serial returner detection, receipt authenticity scoring, duplicate matching. Assigns resolution with confidence scoring. Low-confidence routes to humans.
Customer Communication & Logistics Agent
Proactive status updates, resolution notifications, shipping labels, return instructions. Powers real-time self-service portal for claim tracking.
Outcomes
Outcomes
Metrics
Metrics
Before
Before
After (Agentic AI)
After (Agentic AI)
Impact
Impact
Resolution cycle
Resolution cycle
10–14 days
10–14 days
< 24 hours
< 24 hours
< 24 hours
90% faster resolution
90% faster resolution
90% faster resolution
Error / inconsistency rate
Error / inconsistency rate
15%+
15%+
< 1%
< 1%
93% fewer errors
93% fewer errors
93% fewer errors
Fraud detection
Fraud detection
~3% caught
~3% caught
~12% caught
~12% caught
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
Customer experience
Customer experience
No visibility
No visibility
Real-time self-service
Real-time self-service
60%+ reduction in calls
60%+ reduction in calls
60%+ reduction in calls
Product quality feedback loop
Defect assessment agent surfaced batch-level failure patterns feeding directly into product engineering, enabling proactive action on two product lines.
Retail partner alignment
Standardized claim processing across retail channels reduced disputes and chargebacks by 40%.
AI Accelerator
AI Accelerator
Pre-configured agent templates for warranty and returns processing, retail partner integration adapters, fraud detection model baselines, and computer vision defect assessment models. Estimated 6–8 weeks from kickoff to production.
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
Rewriting critical bottlenecks for scale and performance.
Refactor (For Constraints)
AIspresso Labs
AIspresso Labs
AIspresso Labs
How a Leading Consumer Electronics Brand Transformed Warranty Claims with Agentic AI
How a Leading Consumer Electronics Brand Transformed Warranty Claims with Agentic AI
Replacing slow, error-prone manual claims processing with an intelligent agent system — resolving 50,000+ warranty claims per year in under 24 hours instead of two weeks, with 93% fewer errors and 4x better fraud detection.
PROJECT STATUS: COMPLETED
PROJECT STATUS: COMPLETED
Results So Far
Results So Far
0%
RESOLUTION SPEED
10–14 days → < 24 hrs
0%
ACCURACY
15%+ → < 1% error
0x
FRAUD DETECTION
3% → 12% caught
0%+
CALL VOLUME
reduction in calls
About The Customer
About The Customer
A leading North American consumer electronics brand specializing in personal care appliances — hair dryers, styling tools, grooming devices, and garment care products. Annual revenue exceeding $500M, with products distributed through major retail partners and D2C channels. Processes over 50,000 warranty claims annually across multiple product lines.
A leading North American consumer electronics brand specializing in personal care appliances — hair dryers, styling tools, grooming devices, and garment care products. Annual revenue exceeding $500M, with products distributed through major retail partners and D2C channels. Processes over 50,000 warranty claims annually across multiple product lines.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
The Problem
The Problem
10–14 day resolution cycle
Claims sat in manual queues for days waiting for photo review, purchase verification, and multi-level approval routing — eroding brand trust
15%+ error rate
Different agents made different decisions on identical claims. Wrong replacements shipped, valid claims rejected, covered defects missed
Only ~3% of fraud caught
Serial returners, receipt manipulation, and cross-channel duplicate claims went largely undetected, costing hundreds of thousands annually
Zero customer visibility
No self-service tracking. Customers called back repeatedly, driving up inbound call volume and compounding agent workload
Disconnected systems
Claim data across CRM, logistics, product databases, and retail portals with no unified view — agents toggling between 4–5 systems per claim
The Solution
The Solution
An Agentic AI system on AWS Bedrock where specialized agents collaborate across the full claims lifecycle — assessing photos, detecting fraud patterns, making judgment calls on edge cases, and coordinating actions across disconnected systems.
An Agentic AI system on AWS Bedrock where specialized agents collaborate across the full claims lifecycle — assessing photos, detecting fraud patterns, making judgment calls on edge cases, and coordinating actions across disconnected systems.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
Claim Intake & Triage Agent
Ingests claims from all channels. Identifies product SKU, validates proof of purchase, classifies urgency. Straightforward claims fast-tracked for same-day resolution.
Defect Assessment Agent
Analyzes photos using computer vision against known defect patterns. Cross-references batch data, recall history, and failure-mode databases. Flags safety concerns for immediate escalation.
Fraud Detection & Resolution Agent
Cross-claim pattern analysis: serial returner detection, receipt authenticity scoring, duplicate matching. Assigns resolution with confidence scoring. Low-confidence routes to humans.
Customer Communication & Logistics Agent
Proactive status updates, resolution notifications, shipping labels, return instructions. Powers real-time self-service portal for claim tracking.
Outcomes
Outcomes
Metrics
Metrics
Before
Before
After (Agentic AI)
After (Agentic AI)
Impact
Impact
Resolution cycle
Resolution cycle
10–14 days
10–14 days
< 24 hours
< 24 hours
< 24 hours
90% faster resolution
90% faster resolution
90% faster resolution
Error / inconsistency rate
Error / inconsistency rate
15%+
15%+
< 1%
< 1%
93% fewer errors
93% fewer errors
93% fewer errors
Fraud detection
Fraud detection
~3% caught
~3% caught
~12% caught
~12% caught
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
Customer experience
Customer experience
No visibility
No visibility
Real-time self-service
Real-time self-service
60%+ reduction in calls
60%+ reduction in calls
60%+ reduction in calls
Product quality feedback loop
Defect assessment agent surfaced batch-level failure patterns feeding directly into product engineering, enabling proactive action on two product lines.
Retail partner alignment
Standardized claim processing across retail channels reduced disputes and chargebacks by 40%.
AI Accelerator
AI Accelerator
Pre-configured agent templates for warranty and returns processing, retail partner integration adapters, fraud detection model baselines, and computer vision defect assessment models. Estimated 6–8 weeks from kickoff to production.
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
Rewriting critical bottlenecks for scale and performance.
Refactor (For Constraints)