GenAI-Driven Defect Management System for High-Precision Manufacturing

The client is a US manufacturing company operating in highly regulated, security-sensitive industries, including military and aerospace. They produce precision components for defense systems, space industry, and other mission-critical applications.

Their operations rely on a versatile, cloud-based manufacturing management platform that supports the full production lifecycle—from development concepts to controlled, scalable production. The platform integrates quality management, procedural control, inventory handling, and complete material traceability. With rapid onboarding capabilities, the company can easily transition from basic office tools or underperforming systems without production downtime.

In high-stakes manufacturing environments, even minor defects can lead to delays, increased costs, and compliance risks. When a defect occurred, engineers had to manually analyze historical data to determine whether the part should be repaired, reprocessed, or scrapped.

This resulted in:

• slow, repetitive decision-making,
• inconsistent outcomes depending on individual engineers,
• operational inefficiencies and throughput delays,
• constraints related to data access and confidentiality,
• repeated manual work for every new defect report.

The company needed a secure and compliant way to accelerate corrective-action decision-making while maintaining full human oversight.

We developed an GenAI-powered decision-support layer embedded directly into the client’s secure production environment. The approach focused on:

• leveraging Microsoft Azure as the cloud foundation for secure deployment,
• integrating OpenAI models to power the agent’s core decision engine,
• using Qdrant and Azure AI Search as vector database to store and retrieve historical defect embeddings,
• building orchestration with Python and LangGraph,
• ensuring all data flows remain compliant with strict security and access-control requirements,
• keeping human operators in full control while enhancing their decision-making capabilities.

This ensured high performance, traceability, and full alignment with the client’s confidentiality restrictions.

We implemented an AI recommendation engine seamlessly integrated into the existing cloud-based manufacturing platform.

Technology components

• MS Azure – secure infrastructure, model hosting, data pipelines.
• OpenAI – core inference engine providing contextual recommendations.
• Qdrant, Azure AI Search – vector search database storing embeddings of past defects and successful corrective actions.
• Python + LangGraph – orchestration, prompt management, and data processing logic.

Operational flow

• Defect ticket creation (human-operated)
  A human operator identifies a defect and logs the issue in the system.
• AI analysis
  The engine retrieves relevant historical cases from Qdrant/ Azure AI Search, processes them through OpenAI models, and evaluates similar past outcomes.
• Recommendation generation
  The system proposes the most suitable corrective action, such as re-polishing, reprocessing, repairing, or scrapping the part.
• Human validation
  Operators review and approve the action, maintaining full oversight and regulatory compliance.

This solution provides consistent, fast, and secure decision support within a confidential and regulated production framework.

The implementation delivered substantial operational improvements:

• Faster corrective-action decisions
  Significantly reduced time required to generate repair orders and determine the right action.
• Lower manual workload
  Repetitive analysis was eliminated, previously requiring engineers to manually review similar historical defects.
• Greater production efficiency
  Accelerated defect handling improved product testing throughput and sped up preparation of components for final delivery.
• Reduced waste and cost optimization
  Better recommendations led to fewer unnecessary scrapped parts and optimized reprocessing.
• More consistent, audit-ready decision-making
  The AI engine provides uniform, traceable recommendations aligned with confidentiality rules and regulatory requirements.

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