Industrial Sector
Cuantify AI’s breadth across Automotive, Manufacturing, Energy & Utilities, Mining & Metals
Technology & Expertise
- GenAI
- Hyper-Personalization
- Advanced Analytics
- Edge Computing
- Cloud Migration
- Industrial IoT
- RPA
- Digital Twin
- Geospatial Intelligence
- Blockchain
- Predictive Maintenance
- Connected Workforce
Automotive
Monetizing digital services
Using cloud, IoT and data analytics to gather and analyze data to learn more about consumers’ driving habits and preferences and experiment on different models to generate revenue and profit.
Mobility Services
Delighting passengers with intelligent and self-serving solutions for more business value and deeper insights. Building applications to monitor in-life services – telematics, maintenance, tire management, etc.
Supply Chain Management
Strengthening accountability, improving verification processes and traceability, boosting regulatory compliance by using Blockchain to track parts and components from suppliers to delivery.
Predictive Maintenance
Reducing downtime and costs by analyzing real-time data from vibration, oil sensors and IoT devices and build machine learning algorithms and identify potential machinery breakdowns.
Vehicle Health Workbench
Using AI and ML optimization algorithms to predict failures and schedule preventive maintenance for fleet vehicles, reducing downtime and optimizing maintenance costs.
Dealer Management
Modernizing dealer management with AI and Data Analytics and bringing transparency and new insights across inventory, contacts, leads, sales and revenue management.
Manufacturing
Infrastructure Modernization
Re-building the clients’ IT estate from the ground up, modernizing applications and delivering new cloud-native applications, prioritizing its cybersecurity and data.
Smart Manufacturing
Accelerating and scaling cloud-based digitization that provides scalability, speed, and organizational agility based on lean processes.
Intelligent Operations
Re-designing operations across the entire value chain – agile engineering, connected devices, AI and improving asset management and optimizing performance.
Digital Supply Chain
Leveraging data and advanced technologies such as robotics, machine learning and generative AI to enhance the visibility of the entire supply chain including operations, suppliers and vendors.
Digital Twin
Increasing asset reliability and build a smarter, more efficient operations model to harness change using big data, real-time connectivity and digital twins.
Demand Forecasting
Reducing both overstocking and understocking and reduce carrying costs with predictive analytics to forecast demand.
Energy & Utilities
Manage Field Assets
Enabling E&U firms to enhance their asset management and improve overall operational efficiency with real-time asset, locations and conditions tracking.
Data-driven Transformation
Making smarter, faster decisions. Leverage BI, ML and IoT to be more data-driven in the business decisions, improve revenues, and provide a more tailored customer experience.
Regulatory Approvals
Leveraging AI, geospatial data and solution accelerators powered by AWS and Azure to meet regulatory obligations and for faster-go-to-market.
Digitizing Land Services
Future-proofing the current land services for Energy firms which hold leases for millions of acres by streamlining and upgrading traditional and manual business processes across different functional areas.
Workflow Automation
Minimizing downtime, enhance asset reliability, and optimize resource allocation by facilitating efficient asset maintenance and automating work order management processes.
Connected Field Workforce
Empowering the field team with AI and data from satellite images, drones and other sources and lower cost and risk than in-person inspections.
Mining & Metals
Cloud Transformation
Moving data and workloads to the cloud and scaling computing resources up or down based on fluctuating market conditions. Also developing new applications and quickly bringing to market new business capabilities.
Optimizing Extraction
Optimizing extraction processes by identifying potential bottlenecks, adjusting operational parameters and predicting ore grades with advanced analytics techniques, such as machine learning and predictive modelling.
Digital Twins Simulation
Minimizing downtime, extending asset lifespan, and maximizing operational efficiency with digital twin and advanced analytics, enabling predictive maintenance, real-time optimization, and accurate production forecasting.
Business Intelligence
Centralizing P&L and certain supply chain functions to support customer segmentation by using real-time data to prioritize or reprioritize customers based on profitability, growth potential, ease of servicing, etc.
Energy Management
Optimizing energy consumption in processing operations by analyzing energy usage patterns and equipment performance data to identify energy-intensive processes and implement energy-saving measures.
Process Automation
Implementing Robotic process automation (RPA) and AI to automate many supply chain functions and reduce costs and the potential for human error that could cause delays.