ThinkGink
AI predictive analytics dashboard on a modern screen
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Supply chain network visualization
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Predictive Maintenance Intelligence Suite

A comprehensive AI platform designed to forecast equipment failures and optimize maintenance schedules in industrial environments, reducing downtime by up to 40%.

$4,200 / annual license

Volume and enterprise pricing available. Includes first-year support.

Core Capabilities

  • Real-time anomaly detection across 50+ sensor data types
  • Failure probability forecasting with 94% accuracy
  • Automated, optimized maintenance work order generation
  • Spare parts inventory prediction and integration

Technical Specifications

Data Integration
API, CSV, SQL, IoT Streams
Model Update Frequency
Continuous (weekly retraining)
Deployment
Cloud (AWS, Azure) or On-Premise
Compliance
ISO 55000, GDPR-ready
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AI-Powered Predictive Maintenance Solutions

Explore our portfolio of machine learning models designed to forecast equipment failures and optimize maintenance schedules, reducing downtime by up to 40%.

AI analyzing industrial sensor data on a dashboard
Real-Time Analytics

Sensor Anomaly Detection System

A deep learning model that processes live sensor data from manufacturing lines to identify deviations and predict potential failures weeks in advance.

Technologies: LSTM Networks, TensorFlow
3D visualization of a supply chain network with data points
Logistics Forecasting

Supply Chain Disruption Predictor

Leverages external data (weather, geopolitics) with internal logistics metrics to forecast delays and suggest optimal rerouting for cargo.

Technologies: Gradient Boosting, Python
A robotic arm on an assembly line with data overlays
Operational Automation

Automated Quality Control Vision AI

Computer vision system that automatically inspects products on the assembly line for defects, improving accuracy and throughput.

Technologies: Convolutional Neural Networks, OpenCV

Need a custom analytics solution?

Our team specializes in building tailored AI models for complex industrial challenges.

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