Enterprise AI
Simplified.
Implement cutting-edge Artificial Intelligence, simply. Without any explicit modelling and coding required.
Democratization of AI is not a Myth.
Deep Learning, Reinforcement Learning, Data-Driven Optimization. All these powerful technologies are available right at the fingertips of leaders of enterprise innovation!
Fast. Affordable. Sustainable.
Accelerated innovation cycles, faster prototyping and testing, immediate business results, budget-friendly development, minimized non-value-added effort. In brief, that's AI as a Service.
AI as a Service
Quant Co’s AI as a Service products set help innovation leaders at enterprises who have limited or no AI skillset in their team to build their own data-driven and intelligent control and prediction solutions in a pretty simple and fast way, using pre-built yet customizable models than can self-train.
Step.1: Customize a model according to your business case problem
Get help from the virtual Modeling Assistant in defining the right pre-built model and customizing it based on your business case needs. For certain use cases, you can benefit from off-the-shelf models that require almost no customization.
Step.2: Model self-trains to adjust itself to your business case and its dataset
The customized model that suits the needs of your business case self-trains in order to fine-tune its set of parameters that results in the best solution for your business case problem.
Step.3: Once the training is done, deploy the model and scale your application
Not months, not weeks. In a couple of days or even hours, your proprietary model will be ready to deploy, to get immediate results and feedback and after that to scale for commercialization.
Industry Applications
Energy | Power & Heating
Personalize Energy Service and
Tackle Decentralization Challenges.
Provide hyper-personalized heat and power services for enhanced comfort with the least possible cost.
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Forecast electricity load demand, optimize battery charging policy with respect to fluctuating solar power and set a dynamic pricing strategy for e-mobility services.
Related use cases:
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Forecasting uncertain load and supply
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Load disaggregation and behavioural analysis of energy consumption
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Heat loss/gain estimations and peak demand optimization, e.g., peak management at district heating networks
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Real-time control, e.g., battery charging, frequency response
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Operations scheduling, e.g., e-mobility fleet charging
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Optimized energy consumption for reduced carbon footprint
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Predictive maintenance for boilers
Manufacturing, Supply Chain & E-Commerce
Stay Lean with Minimal Inventory and Manage Resources Efficiently.
Redefine inventory management, dynamically optimize inventory levels and become robust to unforeseen business conditions.
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Implement resource optimization through predictive analytics in order to autonomously fix inefficiencies to maximize budget utilization.
Related use cases:
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Demand and trend forecasting
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Optimized job scheduling
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Robust inventory management under uncertain demand
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Intelligent auto-scaling in dynamic cloud environments for e-commerce
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Real-time bidding optimization in online display advertisement
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Predictive maintenance for the machinery
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