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Simple answer: Automate tasks which can be performed more efficiently by robots and augment your data analytics capabilities.

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Artificial Intelligence

Artificial Intelligence (AI)

The study of how to train computers so they can handle tasks traditionally attributed to humans. In other words, it means training computers so that someday they can take over tasks in which they are more efficient than humans.

A landmark application of AI has been in x-ray analysis, in which a computer can be extra sensitive to light patterns and recognise trends based on a volume of data that is out of the reach of most humans.

Machine Learning

Machine Learning is the process through which an AI entity can learn new things from experience, without the need for being taught. This requires the exposition of AI to a large amount of data with the validation of results being conducted in parallel by both humans and AI itself.

This way an AI can recognise new information on its own and generate new information from apparently disconnected data using self learning algorithms.

Deep Learning

Deep learning is a subset of machine learning in artificial intelligence (AI) that comprises networks capable of unsupervised learning, from data that is unstructured or unlabelled.

Also known as deep neural learning or deep neural network, these algorithms and infrastructures can learn without human dependency. Also known as deep neural.

Robot process automation

Creating RPA processes with Machine Learning capabilities, we can emulate human interacting with software and hardware.
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RPA can work with structured or unstructured data, executing tasks with business logic, with great benefits on processes that have repetitive tasks, particularly those which are time-consuming, prone to human error, predictable, and based on programmable rules.

Robots can be split into 3 major categories:

Probots - Which follow simple and repeatable rules to process data.
Knowbots - Which can be asked to search the internet or an unstructured data pool and gather user-specified information.
Chatbots: Perhaps the most famous of all, consisting in virtual assistants which can interact with users using natural language.

What you can with RPA & Machine Learning ?

Automate routine tasks for users and agents of your organisation, increase the effectiveness and efficiency of your digital processes.
Predictive Analytics

Predictive Analytics is a very complex process requiring multiple techniques and multiple competences to prepare-it so actionable results can be obtained. Usually this process requires multiple statistical techniques ranging from data mining, predictive modelling and machine learning so it can be properly setup.

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Chatbots & Live Interaction

Robots can interact with collaborators, ask questions and perform tasks depending on the answers they receive. According to Chatbots Magazine “A chatbot is a computer program powered by AI that allows you to interact with the customers via a chat interface.”

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Data Capturing & Harmonisation

Capturing data from applications or files, understanding and transforming it, to be fed to other applications or files. The act of data capture consists on the action of gathering data, especificaly from an automatic device, control system, application or sensor.

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Data Fluxes & Integration

Transfer data between applications such as SAP, SalesForce, Microsoft tools, among many others to prevent human errors and time-consuming repetitive tasks that can be performed in parallel between multiple applications and even outside working hours.

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Data Consolidation & Reporting

Creation of reports which are time-consuming can be partly taken over by RPA processes, leaving the user with the strictly necessary analysis and decision-making capabilities.

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Deep Learning

Deep learning is a subset of machine learning in artificial intelligence (AI) focused on networks capable of learning unsupervised from data that is unstructured or unlabelled. Also known deep neural learning or deep neural network – Source Investopedia.

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