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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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Robot Process Automation (RPA)

RPA combines applications of Artificial Intelligence and Machine Learning to create robots which can emulate human interaction using software and hardware.

It encompasses the acquisition of structured or unstructured inputs and the programming of underlying business logic into a robot, allowing it to autonomously execute tasks.

Its application scope is broad but the benefits of RPA generally come from the automation of routine and repetitive tasks, particularly those which are predictable and based on programmable rules.


Robots can be split in 3 major categories:

  • Asset 25 Probots: Which follow simple and repeatable rules to process data.
  • Asset 25 Knowbots: Which can be asked to search the internet or an unstructured data pool and gather user-specified information.
  • Asset 25 Chatbots: Perhaps the most famous of all, consisting in virtual assistants which can interact with users using natural language.

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

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What can you do with RPA and Machine Learning?

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

Find some practical examples bellow.

Data Capturing and Harmonisation

Data Capturing and

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.

Data Fluxes
and Integration

Transfer data between applications such as SAP, SalesForce, Microsoft tools, among many others.

Data Consolidation and Reporting​

Data Consolidation
and 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.

Chatbots and
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.”

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.

Deep Learning

Deep learning is a subset ofmachine 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.


Solutions Design and Architecture

Defining the value. Mapping business processes, operations and technology, shaping ideal solutions and matching your specific needs with the best available technologies and architecting the digital transformation tracks towards the development, implementation, adoption and scale of AI and ML applications, starting with RPA.

Development, Implementation and Maintenance

From Proofs of Concept to Enterprise-grade systems, we can help you across the solutions’ lifecycle, from conception to development, deployment and operation.


Our product Gen.Flow can facilitate the integration of your current infrastructure with technologies offering added levels of automation, such as smart contract applications and distributed or mutualised ledgers.


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