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The convergence of Big Data with Artificial Intelligence has emerged as the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities.

Artificial Intelligence and Machine Learning are two very hot buzzwords right now, and often seem to be used interchangeably. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world. For both, the real value to enterprises depends on data.

Despite this, Artificial Intelligence and Machine Learning are not quite the same thing, but the perception that they are can sometimes lead to some confusion.

Artificial intelligence (AI) is the intelligence exhibited by machines. The term “Artificial Intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as “Learning" and “Problem Solving".

Machine Learning (ML) is a class of algorithms that automates analytical model building and gives computers the ability to learn without being explicitly programmed. Using algorithms that iteratively learn from data Machine Learning allows computers to find hidden insights without being explicitly programmed.

AI & ML:

Artificial Intelligence and Machine Learning are strategically important for driving enterprise strategies.

Every day, new examples are coming out of new problems being solved and old markets being disrupted by what is collectively called “AI”. Broadly speaking, Artificial Intelligence can support three important business needs:

  • Automating business processes
  • Gaining insight through data analysis
  • Engaging with customers and employees

But even as the technology advances, companies still struggle to take advantage of it, largely because they don’t understand how to strategically implement Machine Learning in service of business goals.


It refers to the techniques where a human supported by a machine is trying to extract information and insights from data. This includes predictive models on the higher level.


It is the science of creating algorithms and programs able to learn on their own on the basis of heterogeneous data sources such as systems, things and humans.

artificial intelligence

It is the study of how to create intelligent agents. In practice, it is how to program a computer to behave and perform a task as an intelligent agent (say, a person) would.


After a couple of years of experience within Machine Learning projects there are some lessons that we have learnt.

Read More
  • AI AND ML…

    … they make results when you have a problem in which defining all the possible conditions and rules that are describing that a particular problem requires an infinite amount of time. An algorithm can understand how to define all those infinite rules from data.

    AI is about intelligent machines that self-write the infinite set of rules to automate a task from data.


    AI starts from different sources of data: Historical, User Generated and Real Time. To train a deep neural network you need real Big Data.

    Without real Big Data you will not have results with AI.


    If you only have 10 examples of something, it’s going to be hard to make deep learning work. Any business that has tens or hundreds of thousands of customer interactions has enough scale to start thinking about using these sorts of things.

    «Deep learning takes at least 100,000 examples» - Google Brain chief, Jeff Dean.


    Our people can easily do it in a couple of days during an hackathon. But since a bot is hiding complexity, and the fantasies of customers about them are many, it is really hard to meet expectations without experience and the required skills.

    Chatbots are easy to setup, but complex to deliver at the level of customer expectation (and fantasy).


    If a customer wants a bot or a smart app without the proper time for training it we run the risk of a disappointing result. And since AI & ML take time, the customers need to understand the sooner they start, the better it is.

    The early adopters of AI and Machine Learning in analytics will gain a huge first-mover advantage.



We have entered a promising new era of computing, where advances in machine learning and artificial intelligence are creating a resurgence of interest in conversational interfaces and natural language processing.
This boosts potential for conversation as the new mode of interaction with technology.

Conversational Systems are intelligent machines capable of understanding language and conducting a written or verbal conversation with a user. Their objective is to provide informed answers, assistance, help in direct channel interaction and possibly in real time.

The adoption of Conversational Systems is aimed at improving Customer Experience by steering Human-Machine Interaction.


Conversational systems are designed for conducting a conversation via auditory or textual methods, convincingly simulating how a human would behave and taking advantage of sophisticated Natural Language Processing and Understanding capabilities.

Reply framework Robotics for Customers introduced a Human centered design approach for conversational interfaces, aimed at creating “experience systems”, by humanizing processes and exploiting advanced technology. Besides, the Personality by design approach is the methodology aimed at shaping Bot personality, thus enhancing design of interaction styles, by humanizing Customer touchpoints and augmenting user experience.

Download Brochure about Conversational Systems and Reply’s Solutions


Conversational interfaces, based on voice interaction or chat, are rapidly spreading in common use. Messaging platforms allow interaction with chatbot while smart speakers (Amazon Echo, Google Home, etc.) have quickly spread today.

Reply develops conversational agents in the field of customer care and personal assistant. In the world of customer care, there are several applications of chatbots to increase the effectiveness of the help desk services. Personal assistants provide support in daily tasks.

Read more about Chatbots


E-commerce and retail trade continue to develop. In the past, ongoing development was often driven primarily by technological progress, but this is no longer the case, as the focus nowadays is often placed on the customers and their changing behaviour when it comes to buying.

Input/output devices for voice-supported shopping are already available and can be exchanged as and when required: the smart watch on the wrist, voice recognition in the car or even specialised voice assistants like Amazon’s Alexa or Google Home.
If natural language cannot be used due to, for example, the current situation or the surroundings, informal written orders can also be placed via messengers such as WhatsApp or Telegram. Based on SAP Hybris as a Service (YaaS), Reply Voice Commerce provides a package that can be used to communicate with various services on the in/output devices via a generic interface.

Read more about Reply Voice Commerce


One of the first cases where the Robotics for Customers approach has faced Chatbots has been in the automotive industry, where online assistants have been conceived for product presentation and catalogue configuration.

The way people shop for cars has changed markedly

For the automotive sector, Reply is offering a chatbot solution to instantaneously and fully automatically handles customer interactions dealing with a wide range of topics such as car configuration, customer survey, after-sales service.

Chatbots have the potential to convincingly mimic human actors and even pass the Turing test


Reply has developed a new customer care service using the chatbot technologies for a leading multi-utility company. In addition to its ability to interact with a human speaker, the chatbot implements an end-to-end process that identifies and extracts the information required by the user from back-end systems, supplying this information in real time within the context of a conversation carried out in natural language. The interaction between the user and the chatbot is immediate and does not require login procedures, resulting in a marked improvement of the service offered to the customer.

The New Challenge for Contact Centre Automation


In recent decades, we have witnessed the emergency of an increasingly robotic society and the growth of complex artificial intelligence. We are in a world where the conversation is the interface and the personality is the new User Experience.

Conversations with bots follow the same rules as communication between people

Data Driven Machine Learning Robots

Intelligent Process Automation

Data Robotics, defined as the set of technologies, techniques and applications required to design and implement a new automation process based on self-learning and artificial intelligence technologies, facilitates the introduction and integration of automation into organisational processes. Thanks to the application of “smart” technologies Intelligent Process Automation guarantees an improvement of Robotic Process Automation, facilitating the evolution from solutions that handle straightforward and recurring tasks, to new paradigms based on machine learning techniques.

Intelligent Process Automation 0



Today’s cutting-edge technology already allows businesses not only to look at their historical data but also to predict behavior or outcomes in the future—for example, by helping credit-risk officers at banks to assess which customers are most likely to default or by enabling telcos to anticipate which customers are especially prone to “churn” in the near term (exhibit).

Prescription is the most advanced stage of Machine Learning, because it is, after all, not enough just to predict what customers are going to do; only by understanding why they are going to do it can companies encourage or deter that behavior in the future. Technically, today’s machine-learning algorithms, aided by human translators, can already do this.

Some samples of Reply’s experience: Machine Learning for Fraud Fighting and Insurance Fraud Detection via Unsupervised Learning Methods



From an organization perspective, a Recommendation System can be built on a small amount of data, which can be as diverse as an organization may hold in specific business domains.

Reply introduced a unified development strategy for Recommendation Systems which allows different kinds of services to be developed and implemented in a straightforward manner, from prototypes to production environments. The data processing engine is provided by advanced analytics, typically built using AI-driven and Machine Learning capabilities. Download Brochure about Recommendation Systems and Reply’s Solutions

Some samples of Reply’s experience: Bringing Recommendation Engines in Production Environments and The Banca Mediolanum Case


Image and Video Recognition

Reply adopts innovative Deep Learning techniques for the recognition of images and videos. These techniques, based on neural networks (eg Convolutional Neural Network) allow the use of networks pre-trained on general datasets, or the creation of customized networks on specific datasets. It is so possible to realize recognition engines that allow the identification of specific objects and / or features in videos and images, and the characterization of the sentiment of facial expressions, too.

Reply already developed projects in these field, including the recognition of sentiment in the customer care field, augmented reality (recognition of specific objects to guide the application logic), visual verification of anomalies, the counting of objects on the shelves, etc.

Written and Spoken Language Recognition

Language identification is a Machine Learning technique that allows not only the conversion between voice and text, but also the understanding of the meaning of the text itself. Deep Learning, and specifically frameworks like TensorFlow, are used today to create sophisticated learning models.

Reply has gained the skills for the integration of smart speakers and the know-how necessary for the realization of language recognition models based on the most advanced platforms. Semantic search engines together with machine learning algorithms support the identification of the most relevant results to the requests.

Featured Business Case: Natural Language Processing across the Automotive Value Chain


Best Practice


The challenge has been and remains the skill of determining what to automate, how to automate it and most importantly when to give up, to produce a pipeline of automation. Find out about the challenges of process automation.

Process Mining

Best Practice

X-ray and Optimization for Processes

Process Mining, the Mining of Process Data puts IT systems under scrutiny searching for the digital footprint left by processes. Through an unprecedented level of transparency, Process Mining creates the basis for Process Insights & Optimisation, Automation Initatives or documentation in audit scenarios.

X-ray and Optimization for Processes 0



Tech trends 2019

FleishmanHillard analyzed more than one billion conversations on Twitter and interviewed over 25 global technology leaders about the fads, fears and opportunities of tech trends such as: AI, AR, Edge Computing, Immersive Reality, Blockchain and Quantum Computing.


Tech trends 2019  0




AI: The beginning of the end of life as we know it? Or is it an unheralded force for good?

Find out the fads, the fears and the future in the FleishmanHillard's new report.



Best Practice


X-RAIS is an AI tool for the analysis of medical images based on neural networks, developed by Laife Reply. X-RAIS supports doctors in the medical record compilation phase, automatically calling attention to suspicious areas and the related classification, with the aim of reducing the number of incorrect diagnoses and improving the efficiency of the end-to-end diagnostic process.


Data Robotics Accelerator

Automated Invoice

Automated Invoice is the solution, also available as a service, which facilitates the automated management of the accounts payable process, from the posting phase to the reconciliation between invoices and purchase orders/delivery notes/receipts, highlighting the differences identified.

Data Robotics Accelerator

Brick Machine Learning

Brick Machine Learning is the solution that makes it possible to simulate various configurations used in automated production lines, in order to recommend the optimal mix of devices required to achieve the overall performance requested by the end customer.

Data Robotics Accelerator

Customer Recovery

Customer Recovery is the solution that faces the challenge of behavioral approach on credit risk management. The solution is developed on Microsoft Azure Machine Learning, the service that allows building and testing powerful cloud-based predictive analytics.

Robotic Process Automation

RPA – LAUNCH YOUR digital Transformation

Reply conducted a study that covers the feedback of experienced users of Robotic Process Automation (RPA) as well as RPA beginners. RPA is still in an early stage of the adaption and development process. But it is expected to be a door opener for several other digital game-changing technologies. Learn more about the insights we gained about RPA.

RPA – LAUNCH YOUR 	digital Transformation 0

Data Robotics Accelerator

Employee Monthly Expenses

Employee Monthly Expenses is the solution that facilitates the automated creation of expense reports starting from the underlying cost items, quickly and without the need for manual intervention.

Data Robotics Accelerator

Know Your Orders

Know your Orders is the solution that makes it possible to create a simple interface which can be consulted by users using a natural language, thus facilitating access to information while ensuring consistency and accuracy.

Data Robotics Accelerator


Match-up is an advanced tool for the analysis, reconciliation and matching of complex data (single and/or multiple). The use of this tool finds application in data-related processes.

Data Robotics

Hot Spot

The First Management Program on Intelligent Process Automation

Reply and SDA Bocconi have founded The Data Robotics Lab, a joint initiative which has gathered and developed a significant amount of research evidence and carried out projects for companies on Data Robotics, on an international basis. Based on the research, this executive program is developed to give to business leaders, innovators, and future thinkers with an interest in Data Robotics a fundamental understanding of the impact of the implementation of an intelligent process automation solution on processes, business and efficiency thanks to a personalized roadmap.

The First Management Program on Intelligent Process Automation 0

Machine Learning

Best Practice

Applied Machine Learning: Platforms & Scenarios

Machine Learning is quickly becoming a reliable and scalable set of technologies that can be applied to many business sectors, providing the ability to automate processes and to make applications more intelligent. Reply supports companies to leverage the potential of Machine Learning and Artificial intelligence to deploy industry and business specific solutions such as: Data Robotics, chatbots or predictive engines.

Artificial Intelligence

Best Practice

Towards Augmented Intelligence

Artificial Intelligence and Augmented Intelligence have entered the business realm, been the subject of academic and online discussions and captured the collective imagination with great enthusiasm. Target Reply explains how Artificial and Augmented Intelligence can bring increased value to customers in different areas of application, showing the techniques and technologies applied to real use cases.

Conversational Systems

Best Practice

Conversational Systems in the Automotive industry

One of the first cases where the Robotics for Customers approach has faced Chatbots has been in the automotive industry, where online assistants have been conceived for product presentation and catalogue configuration.

Reply Framework

Hot Spot

Robotics for Customers is here!

Reply has built its own Robotics for Customers approach in the context of Data-Driven Customer Engagement. Robotics for Customers is a framework built on two foundational pillars: Recommendation Systems and Conversational Systems.

Robotics for Customers is here! 0
Human Centered Design The biggest obstacle to customer centricity is your organizational culture 0

Conversational Systems

Best Practice

Human Centered Design: The biggest obstacle to customer centricity is your organizational culture

With the current hype on customer centricity going strong, a lot of companies are asking Reply to help them turn their product and service development towards a more human centered one.

Recommendation Systems

Best Practice

Reply's approach and methodology for bringing recommendations in production environments

Reply has developed the framework Robotics for Customers which allows customers to build a time-to-value Recommendation System that can be easily integrated into any existing platform.

Using Deep Learning and Knowledge Graphs to anticipate customers’ needs 0

Recommendation Systems

Best Practice

Using Deep Learning and Knowledge Graphs to anticipate customers’ needs

There is no mystery behind traditional collaborative algorithms: they simply try to suggest similar content to what we have previously watched, or what other users with similar tastes to us have been watching. Yet we can go even further using Deep Learning and Knowledge Graph methods that leverage contextual and unstructured data.

Prediction & Prescription

Case Study


Reply supported one of the first Consumer Credit Company in Italy with millions of loans every year.

Target Reply‘s solution anticipates and automates fraud detection. It identifies serial fraudsters that change their habits to evade controls and creates more advanced and predictive models that fit in new and unknown contexts.

The Banca Mediolanum case Data Analytics Laboratory and implementation of a Recommendation Engine  0

Financial Services

Case Study

The Banca Mediolanum case: Data Analytics Laboratory and implementation of a Recommendation Engine

With the aim of experimenting with an Advanced Analytics approach, the Data Analytics laboratory initiative launched by Banca Mediolanum involves a partnership between the Marketing Research team and Reply for the development of advanced data analysis mechanisms and the design of proactive services, tailored to the customer’s needs.

Prediction & Prescription

Case Study


Reply supported a large insurance company to identify potential fraudulent users.

Data Reply developed an unsupervised anomaly detection engine to separate fraudulent users from honest ones, so that no righteous person would be suspected to be guilty of an offence.

Humanoid Robots Developement

Best Practice

“Hi, I'm Pepper! How can I help you?”:
The Pepper Experience

Sprint Reply and Bitmama are the two Group companies which have become a centre of expertise focused on the design of the Pepper humanoid robot, thanks to the partnership with Softbank Robotics, a leader in the non-industrial humanoid robots sector.

“Hi, I'm Pepper! How can I help you?”The Pepper Experience 0


Case Study


Reply supported a big German automaker with millions of enquiries about their products.

Data Reply developed a multi-threaded text analytics service that takes the stream of text documents, applies NLP methods to retrieve significant entities and keywords, clusters the documents hierarchically and generates intuitive labels.


News & Communication

What Women Want, "AMY" knows - Triplesense Reply and Machine Learning Reply create the Lancia Ypsilon chatbot experience

Lancia Ypsilon has always been right by women's side. So, to celebrate this special affinity, has decided to present on the 8th of March the first car configurator that understands women and talks like them.

Conversational Systems

Best Practice

Personality by Design

In recent decades, we have witnessed the emergency of an increasingly robotic society and the growth of complex artificial intelligence.

We are in a world where the conversation is the interface and the personality is the new User Experience.


Best Practice

Chatbot for Automotive

Reply supports customers in the automotive industry by implementing ChatBot applications for Car Configurators, After Sales Services and Customer Interaction Center Support.

Chatbot for  Automotive 0


Best Practice

The new challenge for Contact Centre Automation

Syskoplan Reply has developed a new customer care service using the Chatbot technologies for a leading multi-utility company. The chatbot represents a new communication channel, addressing users’ need for reliable and immediate answers made available thanks to the use of Artificial Intelligence.

Big Data Analytics

Case Study

Big Data Analytics for Automotive

Data Reply is supporting CNH Industrial in a project aimed at collecting and analyzing telematics data coming from industrial vehicles, enabling the company to anticipate customers’ needs and offer customized after-sales services.

Big Data Analytics for Automotive  0

Reply Voice Commerce

Best Practice

Conversational Commerce Extension for SAP Customer Experience

Customers are looking for new, interactive buying experiences and offers geared to their needs. At the same time they want to make the replenishment process as efficient as possible, especially when it comes to every day products. With Reply Voice Commerce, Syskoplan Reply has developed an extension for SAP Customer Experience that precisely addresses this need for simplification, and with which language can be used as a natural communication medium.

Conversational Commerce Extension for SAP Customer Experience  0

Machine Learning Tools

Best Practice


A chatbot is not just about technology. A chatbot is a new, customer-oriented communication channel that uses machine learning tools to connect the company with its stakeholders. Machine Learning Reply offers an approach that starts with the analysis of the application itself, to better understand purpose for which the chatbot is being developed.


Operational Risk & Data Robotics

White Paper

Operational Risk & Data Robotics

Data Robotics Solutions are emerging as a highly effective, yet practical approach for banks to reduce operational risk, improve efficiency, reduce costs and derive additional value. From Robotic Process Automation to machine learning enabled Intelligent Process Automation, banks that have started implementing these solutions are reaping the rewards, both from a financial and compliance perspective.

Operational Risk & Data Robotics 0


Best Practice

CIC Chatbot

Cluster Reply supports automotive companies in enhancing the customer service experience by the development of chatbot solutions that are connected with CRM and CIC systems and can seamlessly interact with call center agents. Automotive companies benefit from chatbots as cost-effective way to reduce call center times, increase customer satisfaction and create upselling potential.