AI platform for Finance


AI platform for Logistics & Transportation

Logistics & Transportation

AI platform for Autonomous Systems


AI platform for Decision-support simulations

Decision-support simulations

AI platform for Smart-cities


Introducing VUKU, our AI
Platform for better decisions

Includes all tools & technologies for
decision-making in the real world

Platform components





Spatio-temporal model

GP state space model

Deep GP

Tuneable AI

Hierarchical RL

Risk-sensitive RL

Model-based RL

Bounded rationality

Mechanism design

Demand matching

Resource allocation

Deployable decision-making system

Cloud learning system

Our AI platform is ideally suited for autonomous decision-making in complex, dynamic and uncertain environments, such as financial markets, transportation & logistics, ridesharing, smart cities and robotics. It includes a comprehensive set of AI patent-pending tools and technologies to address varied requirements across all these applications.

The AI platform takes environmental data to create various probabilistic models. It can create spatio-temporal models for applications like ride sharing where the supply and demand for rides is dynamic and distributed across a city.’s advancements in the realm of Gaussian Processes allow for faster and more efficient learning than conventional parametric machine learning based approaches. These probabilistic models are used for learning by Reinforcement Learning (RL) agents to plan strategies or policies for maximizing rewards in a given environment. As with humans, rationality cannot be assumed to be absolute for RL decision-making agents.

The Tuneable AI extends the RL algorithms by bringing in the notion of bounded rationality. Most industrial applications can best be represented by multiple agents, each either competing or collaborating for resources. For example, each vehicle in the city can be represented as an RL agent that understands the other RL agents (vehicles) and collaborates with others to maximize the rewards (revenues) for the ride-sharing company across the city. The demand matching and resource allocation tools allow such applications and others to have multiple agents collaborate or compete to create optimal decisions and maximize returns.

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Our AI Platform for Decision-Making.
Flexible. Scalable. Reliable.

Animated Platform Diagram

Platform Overview’s platform (shown in the architecture diagram above) primarily consists of two components - a decision-making component that is responsible for choosing the correct action to take in an environment, and a learning system that continuously learns new predictive models and policies in order to take the right actions. Using a ride-sharing company as an example of a customer, below is a description of how the various components of the platform get used.

Prior to using the platform in production, the ride-sharing company provides historical environment data that is passed in by the router to the environment data store to store in persistent storage, to be used by the probabilistic modelling based model learner. The model learner passes the learned model to the Reinforcement Learning (RL) agent to be used for planning the optimal decision for the next observation.

Once the platform has created models, the ride-sharing company’s software component sends the details of customer demand and location of various vehicles as observations and the reward from the last ride via the Environment API. The Router routes this data to appropriate components of the platform. The Experience Store stores the observations in a persistent store, from where the Policy Learner learns and evolves a new policy that is consumed by a RL agent to generate a decision, which is passed back to the ride-sharing company’s software component waiting for a decision.

Flexible Deployment Model

The platform has been designed and developed for industry use cases. It can provide decisions at scale, with high availability and fault tolerance. It offers flexible deployment architectures to suit the varied needs of different enterprise use cases. The decision-making and learning components can be deployed on different machines, even in different data centres. For customers, such as in finance, that want decisions in the shortest time possible the decision-making system can be deployed in the customer’s data centre while the learning system can be fully managed in’s data centre. The platform can distribute workloads across many CPUs and GPUs to scale to meet changes in demand.

Ready for Research, Development & Production’s AI platform has been built ground-up for researchers and developers. As is typical in any AI system implementation, the platform supports experiment automation at scale, enabling researchers and developers to monitor their experiments and perform post-hoc analysis. The platform tools also allow researchers and developers to record specific conditions necessary to accurately reproduce the results. This allows the developers to then build and configure the platform production environment.

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