AI Inspection Pipeline

Overview

Built an event-driven AI inspection pipeline that automates offshore infrastructure analysis. The system processes images and videos from field inspections, leveraging state-of-the-art vision-language models to generate structured JSON reports for engineering teams.

Technical Architecture

Event-Driven Processing

• AWS SageMaker Pipeline: Automated workflow orchestration
• Lambda-Based Orchestration: Serverless event processing
• S3 Event Triggers: Automatic processing on asset upload
• Step Functions: Complex workflow state management

Vision & Language Models

• Claude Sonnet 4.5 (AWS Bedrock): Advanced image understanding and report generation
• TwelveLabs: Specialized video analysis and temporal understanding
• Multi-modal reasoning for complex inspection scenarios
• Structured JSON output for downstream integration

Data Processing

• OpenCV: Image preprocessing and enhancement
• FFmpeg: Video transcoding and frame extraction
• Pydantic: Strict schema validation for outputs
• Automated quality checks and format standardization

S3 Caching Strategy

Implemented intelligent caching layer achieving significant cost reduction:

Cache Architecture

• Content-addressable storage for processed results
• TTL-based cache invalidation policies
• Hierarchical caching (raw to processed to analyzed)
• 30-40% Cost Reduction through cache hits

Cache Optimization

• Hash-based deduplication for similar assets
• Incremental processing for updated content
• Warm cache strategies for common inspection types
• Cost-aware cache eviction policies

Quality Assurance

DeepEval Framework

Comprehensive evaluation pipeline ensuring output quality:

• 20+ Quality Metrics: Accuracy, completeness, consistency
• Faithfulness Scoring: Grounded observations vs. hallucination detection
• Schema Compliance: Structural validation of JSON outputs
• Domain-Specific Checks: Engineering terminology accuracy

Continuous Monitoring

• Automated quality regression alerts
• Model performance tracking over time
• Human-in-the-loop validation sampling
• Quality dashboards for stakeholders

Scalability & Performance

Concurrent Processing

• 100+ Concurrent Executions: Parallel inspection processing
• 95%+ Success Rate: Robust error handling and retry logic
• Auto-scaling based on queue depth
• Priority queuing for urgent inspections

Performance Optimization

• Batch inference for efficiency
• GPU instance pooling
• Async processing with callback notifications
• Graceful degradation under load

Report Generation

Structured Outputs

• JSON Reports: Machine-readable inspection data
• Defect classification and severity scoring
• Location mapping and asset identification
• Temporal analysis for video inspections

Integration Points

• REST API for report retrieval
• Webhook notifications on completion
• PostgreSQL storage for historical analysis
• Dashboard visualization for engineering teams

Technologies Used

• Vision Models: Claude Sonnet 4.5, TwelveLabs
• Cloud: AWS SageMaker, Lambda, S3, Step Functions
• Database: PostgreSQL
• Processing: OpenCV, FFmpeg
• Validation: Pydantic, DeepEval
• Languages: Python, TypeScript
• Infrastructure: Docker, AWS CDK

Impact

• Cost Efficiency: 30-40% reduction through intelligent caching
• Scale: 100+ concurrent inspection processing
• Reliability: 95%+ success rate with robust error handling
• Automation: End-to-end pipeline from upload to structured report
• Quality: Comprehensive evaluation framework ensuring accuracy