How we’ve helped businesses succeed.
Plant uptime increased
This solution, deployed by Hosokawa Micron, supports condition monitoring for preventative maintenance, diagnostics for improved plant & product quality & trouble-shooting bots for minimising downtime. This has resulted in 15 percent improvement in uptime & a 20% reduction in energy usage.
Improvement in plant uptime
Increased call handling
This solution, deployed by a large telecoms company, improves its customer care & technical support. We use data from customer accounts, product details, best practice & problem resolution expertise to create responsive trouble-shooting guides.
Increase in call handling capacity
This solution, deployed at an oil company, used machine learning to understand how energy costs and emissions were dependent on external factors, control settings and operating practices. We identified opportunities for reducing energy costs by 3% and reducing CO2 emissions by 7%.
Reduction in CO2 emissions
This solution, deployed by a Financial Services company, automates the credit authorization process, as well as providing, decision-making support to front-office agents. The solution has led to a double digit increase in sales alongside a reduction in exceptions and poor-quality risk decisions.
Reduction in exceptions
Warehouse efficiency doubled
This solution, deployed by a major drinks manufacture across multiple distilleries & warehouses, improves operational efficiency. The results were a 36% reduction in non-productive container handling.
Reduction in non-productive container handling
Energy costs reduced
This solution, deployed at a UK brewery, used machine learning on data from a central refrigeration system to understand the variations in the efficiency of the plant. This identified opportunities to reduce energy costs by changing the control strategy and operating practices with minimal capital cost.
Annual reduction in energy costs
This solution, deployed at a chemical complex, used machine learning to identify patterns in operating parameters and modes of operation. These patterns were used to determine the optimal control set points and operating strategies required to minimize costs.
Reduction in costs
Complex underwriting automated
This solution, deployed at a major insurer, used data mining & predictive modelling capabilities to define risk profiles, which, when combined with actuarial expert knowledge enabled 80% of all underwriting decisions to be automated. Machine-learning was continually used to refine the underwriting decisions.
Automation of underwriting decisions
Reduction in costs
This solution, deployed at a mineral processing plant, used machine learning to understand and improve the performance of mineral extraction. The model identified potential for a significant increase in performance, delivering optimal control settings as advise to operators & engineers.
Reduction in costs per annum
This solution, deployed at a mineral processing plant, used machine learning to analyse the performance of rotary kiln driers. It identified the best operating conditions to minimise energy costs while still meeting production and quality targets.
Savings of energy use