Stories that balance insight with confidentiality

Challenge

Quality teams relied on manual SPC chart reviews and fragmented root-cause workflows. Out-of-control signals were detected late, investigations varied by team, and audit trails were difficult to reconstruct across production lines.

Solution

An AI-SPC workflow combining rule-based SPC, anomaly detection, and guided investigation steps. The system standardises escalation logic, records decision trails, and supports human-in-the-loop reviews for regulated production environments.

Challenge

Quality and production teams needed fast, reliable access to SOPs, specifications, and audit evidence. Documents were distributed across multiple formats, versions, and shared repositories, increasing the risk of outdated or incorrect usage.

Solution

An OCR ingestion pipeline with structured extraction and a RAG-based search layer over controlled document versions. The workflow enforces access control, review checkpoints, and clear source citations to support audit and compliance use cases.

Challenge

Organizations struggled to capture, organize, and retrieve institutional knowledge efficiently across departments, leading to information silos, duplicate work, and difficulty finding relevant expertise.

Solution

RAG + LLM powered knowledge management system with AI-powered search, intelligent document processing, and personalized recommendations based on user roles and project contexts.

Challenge

Cloud-dependent AI had latency and privacy issues.

Solution

Jetson/RPi edge with on-device LLM; secure offline-capable pipeline.

Challenge

Manual quality inspection was slow, inconsistent, and prone to human error, leading to defective products reaching customers and high inspection costs.

Solution

AI-powered vision system with real-time defect detection, automated sorting, and quality analytics dashboard for production line optimization.

Challenge

Unplanned equipment failures caused production downtime and maintenance costs, with reactive maintenance approaches being inefficient and expensive.

Solution

IoT sensor network with machine learning models for predictive maintenance, automated alerting, and maintenance scheduling optimization.

Challenge

Manual quality inspection was slow, inconsistent, and prone to human error, leading to defective products reaching customers and high inspection costs.

Solution

AI-powered computer vision system using tacit knowledge transfer from expert inspectors to automatically detect defects and sort products with 90%+ time reduction.

Challenge

Environmental conditions in manufacturing facilities needed precise control, but manual monitoring and adjustment was inefficient and often reactive rather than predictive.

Solution

Time-series analysis system that collects environmental data at specified intervals, performs anomaly detection, and automatically controls devices to maintain optimal conditions.

Challenge

Lack of proactive insights and timely anomaly detection from complex data, leading to reactive decision-making.

Solution

Machine Learning-driven data analysis to identify variable relationships, enabling predictive recommendations and proactive anomaly detection for improved operational management.