At a Glance

99% reduction in processing time | 82% cost reduction | 75% claim reassessment improvement | 44% higher customer satisfaction

A Global Tyre Manufacturer and Distributor firm transformed tyre warranty processing from 48-hour manual inspection to under 5 minutes, scaling capacity 100x while dramatically improving consistency.

The Challenge

A Global Tyre Manufacturer and Distributor faced mounting pressure in claims processing as their retail partner network expanded.. The company processes over 5,000 warranty claims monthly for 15+ global tyre manufacturers, serving 200+ retail partners.

Claims adjusters spent 2-3 days per claim manually inspecting tyre images and making subjective decisions. The backlog grew to over 2,000 pending claims, creating 48-72 hour turnaround times. Inconsistent decisions created a 30% inconsistency rate - different adjusters made different decisions on similar claims. No comprehensive audit trail existed.

Scalability limitations threatened growth. Manual processing restricted throughput to 5-10 claims per adjuster per day. The network needed 3x capacity, requiring proportional staff increases that made scaling economically prohibitive. Each manufacturer maintains unique warranty rules, resulting in a 15% rule application error rate.

The Solution: AI-Powered Claims Processing

DevKraft delivered an enterprise-grade AI system in 12 weeks. The solution combines specialized computer vision with contextual AI analysis, transforming a manual process into an automated system processing claims in under 5 minutes.

The multi-LLM architecture leverages OpenAI GPT-5 Vision combined with custom-trained YOLOv8, providing specialized defect detection plus general AI for contextual analysis. This hybrid approach ensures both precision and comprehension of warranty context.

The YOLOv8 system detects 52 tyre defect classes with 90% accuracy. It processes images in 1-3 seconds compared to 15 minutes manually, identifying tread wear, sidewall damage, manufacturing defects, and improper usage indicators.

High-performance infrastructure on FastAPI with PostgreSQL, Redis, and AWS provides 100+ concurrent claims processing with sub-second response times. An intelligent rules engine applies manufacturer-specific criteria through hybrid deterministic and AI-assisted approaches. Hard rules ensure compliance, while AI handles nuanced cases. The system generates confidence scores and escalates edge cases.

Implementation Journey

The 12-week implementation used agile methodology with a 6-person team embedded with ZAFCO's operations.

Weeks 1-3: Assessment, data audit of 10,000 historical claims, system architecture design, delivering architecture blueprint with ROI projections.

Weeks 4-7: Core AI engine development, training YOLOv8 on 52 defect classes, GPT-5 Vision integration, FastAPI microservices, delivering working AI analysis engine.

Weeks 8-10: Complete integration, business rules engine for 15 manufacturer policies, validation with 500 live claims, load testing for 100 concurrent claims.

Weeks 11-12: Production launch, AWS deployment, 3-day staff training, delivering live system at scale.

Transformative Business Impact

Processing time plummeted from 48 hours to under 5 minutes (99% reduction). Processing cost per claim decreased 82% through automation while preserving human expertise for complex cases.

Claim reassessment rates improved 75% as consistent decisions reduced disputes. Customer satisfaction increased 44% driven by faster turnaround with transparent decision-making. System uptime improved to 99.9%.

Concurrent processing scaled 100x from 5-10 claims to over 100 automated claims simultaneously. Decision consistency transformed from 30% variance to standardized criteria. Audit trail capability reached 100% comprehensive automated logging.

Institutional knowledge becomes captured within the AI system, preserving expertise when staff changes. The system reached 95% accuracy in the first quarter post-launch through continuous learning.

Key Innovation: Hybrid AI Architecture

YOLOv8 provides deep expertise in tyre defect recognition, trained specifically on automotive warranty scenarios to identify subtle manufacturing defects and wear patterns that general vision models miss.

GPT-5 Vision adds contextual intelligence, understanding warranty documents, manufacturer policies, and claim context. This AI interprets complex rules, handles edge cases, and provides natural language explanations for decisions.

The intelligent rules engine bridges deterministic and probabilistic approaches. Hard rules enforce explicit warranty terms. AI-assisted evaluation handles gray areas where warranty language permits interpretation. Confidence scoring automatically escalates low-confidence cases to human review.