Moving towards the democratisation of Robotic Process Automation (RPA)

Digitalisation is no longer an option. Since remote working has become the norm, companies need to find new solutions to optimise their business processes and gain efficiency. The pandemic and the acceleration of digitalisation have made Robotic Process Automation (RPA) a key element for organisations. According to a Gartner report, by 2022 90% of large companies will have implemented an RPA project into their operation.

With the democratisation of remote working, it becomes necessary to simplify the daily lives of employees. Today, too many tasks are still repetitive or unstimulating. Who hasn’t wanted to completely automate worthless ‘copy & paste’ from one form to another, when this task has sometimes to be performed more than 10 times a day? By putting together these different tasks and  “processes”, we can create an activity, which makes Robotic Process Automation a relevant solution.


Turnkey Smart Automation

By implementing Smart Automation solutions, companies can not only reduce their costs, but also allow their employees to focus on their core business: value generating tasks.

Heineken is an interesting use case with its “Automation First” strategy. In 2011, starting with the simple observation of the urgency to standardise IT systems of all service centres employing more than 73,000 people, the company focused on the selective automation of its processes at the end of 2015. Heineken decided to couple RPA with other technologies (image recognition, text mining, etc.) to find the right balance for human/machine interactions.

By 2019, Heineken had 150 software robots in production within different areas, implementing Machine Learning and AI algorithms. Without going into detail about the amount of man-hours saved by implementing this ‘Automation First’ strategy, Vincent Vloemans, from Heineken’s Global IT for Finance Business, confirmed a significant impact. “It is quite surprising, but the most significant RPA benefit is the elimination of human error. It increases the accuracy of process data, the accuracy of controls and the compliance of processes”. In concrete terms, a request that used to take 1-2 weeks to process can now be answered within 24 hours. Large CAC 40 companies are already familiar with this Smart Automation strategy. What about mid-sized organisations?


Making automation accessible to all

With a double digit annual growth rate, the Smart Automation market seems to be booming. However, structuring data, digitising documents and optimising business processes through OCR technologies or NLP algorithms can be time-consuming, difficult to implement or too costly for SMEs and SMIs. However, this type of companies has the same growing need to streamline internal processes and automate some tasks.

The role of all digital players is to help make innovations accessible to all organisations regardless of their size. The particular role of RPA players is to imagine new technological and economic models that can help to remove obstacles for implementing Automation in human-sized organisations.

An automation project can involve all departments of an organisation, from finance to human resources and purchasing. For example: within an HR department, the onboarding of new employees can be easily automated. The time needed for completing this task, which involves various cross-functional departments, can be reduced from 80 to 5 minutes.

Moreover, the “Agence de services et de paiement” (service and payment agency) was keen to provide support for industrial SMEs and SMIs wishing to implement RPA projects, by providing investment aid up to €320,000 for transforming towards the future industry.


What future for Smart Automation?

According to Gartner, the RPA (Robotic Process Automation) market is expected to grow by 19.5% in 2021, compared to 2020. And this number should remain in double digits until at least 2024.

In 5 years, we can imagine a large democratisation of access to automation: the most complex processes will be carried out in human-machine interaction. As a result, humans will have more time to focus on high value-added tasks related to creativity and innovation. Freeing activities from superfluous tasks will optimise and improve value creation, reflection, collaboration and exchanges.

Imagine if the machine could call on the user when needed, if it could learn from employees actions to reproduce behaviour rather than precise, preconceived tasks. AI-based approaches would then logically merge with automation and thus erase all the variations that can be found in the execution of the same task. This would usher in a new era, where interactions between information systems would be achieved via automation at scale and where, of course, the notion of the ‘citizen developer’ would become obsolete. In this new era, machines would be able to learn and reproduce uniformely processes carried out in slightly different ways by several business users.

The relationship to work would be completely changed, the traditional working day would be over. The era of “work time” would be over, giving way to that of “work value”. Value and collective intelligence would be free to support the evolving of uses. 

This access’ democratisation to automation will bring about some changes: We can imagine that in just a few days it will be possible to become “RPA Ready”. Everyone will be able to have transparent access to their digital/robotic workforce, from their mobile, in real time and from anywhere.


By Mehdi Nafe, CEO of Novelis and Benoit Cayla, Artificial Intelligence Analyst at Blue Prism. Posted in IT Social (article in French) – Media of IT trends & uses, Tech and Business.


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Chloe VEAUVY July 6, 2021 0 Comments

Novelis is hiring! Discover our Welcome to the Jungle page

We are pleased to announce the publication of our Welcome to the Jungle career page! More than ever, we want to strengthen our teams of technical and operational experts to achieve our goals.

This page reflects the image of Novelis: in addition to offers, you will find information on the values and culture we share with our employees on a daily basis as well as video interviews with 5 Novelian.

From 50 employees at the end of 2019 to 60 at the end of 2020, Novelis now has more than 80 employees and we do not intend to stop there! Created in 2017, our ambition is to become a reference company in Smart Automation and Artificial Intelligence in the coming years. To achieve this, we have decided to focus on human resources and we are constantly looking for new talent.

What kind of profiles do we hire?

All types of profiles. Both technical and operational. Automation Architects, Java Developers, RPA Consultants, Technical Experts, Engineers, but also IT Business Developers, Sales people, Account Managers, Scrum Masters, are some of the positions available at Novelis (see all the offers).

In addition, we have a scientific research and Artificial Intelligence branch in the R&D Laboratory division R&D Lab, for which we are looking for numerous PhDs and technical experts. Since our creation, we have invested considerably in scientific research to be at the state of the art in AI and to develop the technologies of tomorrow.

Beyond technical skills, Novelis is looking for employees who can develop real careers. Our employees share the same values: collective spirit, high standards and innovation. The company’s ambition, through all these shared values, is to involve all employees over the long term, from trainee to engineer.

Our recruitment policy knows no boundaries! We look for talent wherever it can be found. It is the diversity of our employees, their backgrounds, their personalities and characters, and their geographical origins that make up the undeniable wealth of Novelis teams.” Linda Mefidene, Human Resources Manager – Novelis.

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Chloe VEAUVY July 6, 2021 0 Comments

SQL Generation from Natural Language: A Seq2Seq Model – Transformers Architecture

Novelis technical experts have once again achieved a new state-of-the-art in science. Discover our study SQL Generation from Natural Language: A Sequence-to-Sequence Model Powered by the Transformers Architecture and Association Rules, puplished on Journal of Computer Science.

Thanks to the Novelis Reasearch Team for their knowledge and expertise.


Using natural language (NL) to interact with relational databases allows users of any background to easily query and analyze large amounts of data. This requires a system that understands user questions and automatically translates them into structured query languages ​​(such as SQL). The best-performing Text-to-SQL system uses supervised learning (usually expressed as a classification problem) and treats this task as a sketch-based slot filling problem, or first converts the problem into an intermediate logical form (ILF) and then converts it Convert to the corresponding SQL query. However, unsupervised modeling that directly translates the problem into SQL queries has proven to be more difficult. In this sense, we propose a method to directly convert NL questions into SQL statements.

In this research, we propose a sequence-to-sequence (Seq2Seq) parsing model for NL to SQL tasks, supported by a converter architecture that explores two language models (LM): text-to-text transfer converter (T5) ) And multi-language pre-trained text-to-text converter (mT5). In addition, we use transformation-based learning algorithms to update aggregation predictions based on association rules. The resulting model implements a new state-of-the-art technology on the WikiSQL data set for weakly supervised SQL generation.

About the study

“In this study, we treat the Text-to-SQL task with WikiSQL1 (Zhong et al., 2017). This DataSet is the first large-scale dataset for Text-to-SQL, with about 80 K human-annotated pairs of Natural Language question and SQL query. WikiSQL is very challenging because tables and questions are very diverse. This DataSet contains about 24K different tables.

There are two leaderboards for the WikiSQL challenge: Weakly supervised (without using logical form during training) and supervised (with logical form during training). On the supervised challenge, there are two results: Those with Execution Guided (EG) inference and those without EG inference.”

Journal of Computer Science – Volume 17 No. 5, 2021, 480-489 (10 pages)

Journal of Computer Science aims to publish research articles on the theoretical basis of information and computing, and practical technologies for implementation and application in computer systems.

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Chloe VEAUVY June 30, 2021 0 Comments

Novelis’ Smart Roby awarded as Best Blue Prism Business Solution 2021

On May 6 at the 2021 Partner Forum by Blue Prism, Novelis has been awarded for its ‘Robot as a Service’ solution, Smart Roby, granted Best Business Solution of the Year by the leading RPA vendor. The Blue Prism Regional Award is a way of acknowledging Novelis’ ability and excellence in delivering an innovative smart automation solution.

Novelis has designed Smart Roby, a ‘Robot as a service’ solution, with a turnkey-model based on the effective use of the robots. In such, we are giving human-scale structures access to cutting-edge RPA and Smart Automation technologies. To design Smart Roby we have worked with our strategic partner Blue Prism, pioneer and market leader in robotic process automation (RPA). Our solution is highly complementary to Blue Prism’s tools and provides a business-oriented interface to drive and control all automated processes within an organization.

At Novelis, we support more than 50 international customers with daily AI, NLP, OCR and RPA issues. With our Smart Roby solution, we want to make automation accessible to all organizations, regardless of their size. We believe that as a digital player it is our role to enable all businesses to benefit these tools that digitally transform the way organizations operate. With our easy-to-use, centralized, pay-as-you-go model (no licensing nor infrastructure is required), organizations can now automate their processes within a few weeks and at a lower cost.

Winning the Partner Excellence Awards 2021 in the Business Solution category is a true recognition of the innovative nature of our Smart Roby ‘Robot as a Service’ solution. This award also celebrates our commitment to working alongside Blue Prism for supporting organizations on their journey to digital transformation.

Linda Dotts, Chief Partner Strategy Officer at Blue Prism, said:Blue Prism values our partner community for its positive impact on our shared customers. Their solutions built on the Blue Prism intelligent automation platform provide a way for organizations to approach work in a new, more agile way. Our partner awards are a way to showcase and thank our amazing partners for the incredible innovation and support they give to our customers. Congratulations to Novelis for showing us what’s possible and for making Blue Prism truly the sum of our parts.

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Chloe VEAUVY May 11, 2021 0 Comments

Smart Roby: The future of process automation

A turnkey solution to make automation accessible to all organizations

Paris, April 13, 2021 – Novelis, a global consulting and technology company, announces the launch of Smart Roby, its Robot-as-a-Service platform designed to democratize access to business process automation solutions.

Smart Roby home screen

Because of the health crisis, organisations have accelerated their digitalisation and their deployment into the cloud. However, companies and public services in France are still slow at integrating business process automation and document digitization technologies. Too often, this is due to a lack of technological expertise, but sometimes this is also for costs reasons.

As a result, there is an existing need to provide companies with a simple and accessible solution for benefiting the functionality of document automation and digitization software robots. The ultimate goal is to improve productivity, customer experience and satisfaction of the employed.

“Digital players have a responsibility to ensure that technological innovations are accessible to all organizations, regardless of their size. This accessibility is measured both in terms of costs and implementation complexity. Since the beginning, Novelis has included this issue in its strategy and has developed platforms designed to democratize access to Smart Automation solutions and to improve business productivity. Smart Roby is the perfect representation of this”, explains Mehdi NAFE, CEO and co-founder of Novelis.

Smart Roby, a turnkey solution

By combining its various skills in IS architectures, RPA (Robotic Process Automation), OCR (Optical Character Recognition) and Artificial Intelligence, Novelis has designed Smart Roby, a platform which enables automation in a RaaS (robot as a service) modus with a simple invoicing system based on the actual consumption of the robots.

Concretely, Smart Roby provides a turnkey automation service in a matter of weeks. The solution is hosted in the Cloud, freeing organizations from infrastructure constraints and requiring no additional licences. Once the process has been set up in Smart Roby, the organization can easily manage it from a personalized portal, which offers various functionalities such as: process evaluation, ROI management dashboard, exception management, process management, alert management, analytics, user administration, etc.

“The business process automation and optimization approach is a real company project. It makes the information flow within an organization more reliable and fluid. This is a decisive asset, particularly in times of pandemic. We also see beneficial effects for operational teams, who are relieved of tedious and repetitive tasks. These tasks are often spread throughout the organisation and rarely focused on one single employee.” adds Mehdi NAFE.

From now on, companies of all sizes can access the services offered by Smart Roby by visiting

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Chloe VEAUVY April 9, 2021 0 Comments

Novelis ranks 2nd in international NLP Research Challenge

One more step towards the democratization of Artificial Intelligence and NLP (Natural Language Processing)

Paris, March 25, 2021 – Novelis, an innovative consulting and technology company, is currently taking part in two international research challenges aiming to automatically generate SQL queries thanks to natural language. Following the recent publication of its work, Novelis is positioned alongside Artificial Intelligence leaders, such as Microsoft, Salesforce, Google and others.

The worldwide volume of data processed daily has never been so big. These data are mostly gathered in so-called relational databases, which require mastering a Structured Query Language SQL to store or manipulate the aforementioned data. Novelis’ project aims to democratize access to these data by automatically generating technically complex queries from human language, also known as Natural Language Processing (NLP).

Novelis in major international challenges SPIDER and WikiSQL

Led by Yale University, the Spider Challenge brings together a large-scale complex cross-domain semantic data set and SQL queries. The goal is to transform natural English text into executable SQL-queries, also called “Text-to-SQL task”. The Challenge consists of 10,181 questions, 5,693 unique complex SQL-queries on 200 databases with multiple tables covering 138 domains. Following the publication of its work, Novelis has been ranked 2nd worldwide alongside SalesForce, and only 2.9 points behind the leader: Tel-Aviv University & Allen Institute for AI. Follow the link for more information and complete results: Spider: Yale Semantic Parsing and Text-to-SQL Challenge (

The objective of the WikiSQL Challenge is the same as for Spider but with different constraints and contexts. Here, the participants only deal with one table from models with unsupervised learning (where the machine works on its own) or with supervised learning (where the machine relies on hints from which it generates predictions). Leading companies in Artificial Intelligence and NLP are taking part in this challenge along with reknowned universities: Microsoft, Google, Alibaba, Salesforce, the Universities of California, Berkeley, Fudan… For this event, Novelis has developed a hybrid learning model that ranks 7th out of 31 scientific projects. Follow the link for more information and complete results: GitHub – salesforce/WikiSQL: A large annotated semantic parsing corpus for developing natural language interfaces.

Innovation and R&D: A strategic priority for Novelis’ development

Since its beginning, Novelis has been investing massively (30% of its turnover) in Research and Development. According to Mehdi Nafe, CEO of Novelis: “Beyond the impact on fundamental research, our objective is to change the software design model to achieve operational excellence, change the relationship we have with technologies, and have a sustainable impact on innovation processes within society. In the last years, the major progress of data science, AI and, more recently, NLP, represents a huge potential in terms of business process optimization and use. The creation of an R&D Lab is one of Novelis’ founding acts. For a technology company, engaging in research is a key element. It is essential for better serving our customers.”

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Chloe VEAUVY March 29, 2021 0 Comments

NL2Code: A Corpus and Semantic Parser for Natural Language to Code

Discover our conference paper NL2Code: A Corpus and Semantic Parser for Natural Language to CodeInternational conference on smart Information & communication Technologies part of the  Lecture Notes in Electrical Engineering book series (LNEE, volume 684).


Thanks to the Novelis Research Team for their knowlegde and experience.



In this work, we propose a new semantic analysis and data method that allows automatic generation of source code from specifications and descriptions written in natural language (NL2Code). Our long-term goal is to allow any user to create applications based on specifications that describe the requirements of the complete system. It involves researching, designing, and implementing intelligent systems that allow automatic generation of computer projects by answering user needs (skeleton, configuration, initialization scripts, etc.) expressed in natural language. We are taking the first step in this area to provide a new data set specifically for our Novelis company and implement a method that enables machines to understand the needs of users and express them in natural language in specific areas.


About the study

“The dream of using Frensh or any other natural language to generate a code in a specific programming language has existed for almost as long as the task of programming itself. Although significantly less precise than a formal language, natural language as a programming medium would be universally accessible and would support the automation of an application. However, the diversity and ambiguity of the texts, the compositional nature of the code and the layered abstractions in the software make it difficult to generate this code from functional specifications (natural language). The use of artificial intelligence offers interesting potential for supporting new tools in almost all areas of software engineering and program analysis. This work presents new data and semantic parsing method on a novel and ambitious domain — the program synthesis.

Our long-term goal is to enable any user to generate complete web applications frontend / backend based on Java / JEE technology and which respect a n-tier architecture (multilayer). For that, we take a first step in this direction by providing a dataset (Corpus) proposed by the company Novelis based on the dataset that contains questions / answers of the Java language of the various topics of the website ”Stack OverFlow” with a new semantic parsing method.”


Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 684)
SpringerLink provid researchers with access to millions of scientific documents from journals, books, series, protocols, reference works and proceedings.

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Chloe VEAUVY August 4, 2020 0 Comments

Artificial Neural Networks for Text-to-SQL Task: State of the Art

Discover our conference paper Artificial Neural Networks for Text-to-SQL Task: State of the ArtInternational conference on smart Information & communication Technologies part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 684).


Thanks to the Novelis Research Team for their knowlegde and experience.




The database stores a large amount of data from all over the world, but to access this data, users must understand query languages ​​such as SQL. In order to facilitate this task and make it possible to interact with databases around the world, some research has recently emerged to deal with systems that understand natural language problems and automatically convert them into SQL queries. The purpose of this article is to provide the most advanced text-to-SQL tasks, in which we show the main models and existing solutions (natural language deal with). We also specify the experimental settings for each method, their limitations, and a comparison of the best available methods.


About the study

“Text-to-SQL task is one of the most important subtask of semantic parsing in natural language processing (NLP). It maps natural language sentences to corresponding SQL queries. In recent years, some state-of-the-art methods with Seq2Seq encoder-decoder architectures (Ilya Sutskever, Oriol Vinyals, Quoc V. Le 2014) [1] are able to obtain more than 80% exact matching accuracy on some complex text-to-SQL benchmarks such as Atis (Price, 1990; Dahl and al., 1994) [2], GeoQuery (Zelle and Mooney, 1996) [3], Restaurants (Tang and Mooney, 2000; Popescu and al., 2003) [4], Scholar (Iyer and al., 2017) [5], Academic (Li and Jagadish, 2014) [6], Yelp (Yaghmazadeh and al., 2017) [7] and WikiSQL (Zhong and al., 2017) [8].These models seem to have already solved most problems in this area. However, as (Finegan-Dollak et al., 2018) [9] show, because of the problematic task definition in the traditional datasets, most of these mod- els just learn to match semantic parsing results, rather than truly learn to understand the meanings of inputs and generalize to new programs and databases, which led to low precisions on more generic dataset as the case of Spider (YU, Tao, ZHANG, Rui, YANG, Kai, and al. 2018) [10].”


Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 684)
SpringerLink provid researchers with access to millions of scientific documents from journals, books, series, protocols, reference works and proceedings.


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Chloe VEAUVY August 4, 2020 0 Comments

SQL Generation from Natural Language Using Supervised Learning and Recurrent Neural Networks

Discover our conference paper SQL Generation from Natural Language Using Supervised Learning and Recurrent Neural NetworksInternational Conference on Artificial Intelligence & Industrial Applications part of the Lecture Notes in Networks and Systems book series (LNNS, volume 144).


Thanks to the Novelis Reasearch Team for their knowledge and expertise.



The database stores today’s large amounts of data and information. To access these data, users need to master SQL or an equivalent interface language. Therefore, using a system that can convert natural language into equivalent SQL queries will make the data more accessible. In this sense, building a natural language interface to a relational database is an important and challenging problem in the field of natural language processing (NLP) and extensive research, and due to the introduction of large-scale data sets, it has recently been discovered again momentum. In this article, we propose a method based on word embedding and recurrent neural network (RNN), precisely based on long short-term memory (LSTM) and gated recurrent unit (GRU) units. We also showed the dataset used to train and test our model, based on WikiSQL, and finally we showed our progress in accuracy.


About the study

Vast amount of today’s information is stored in relational database and provide the foundation of applications such as medical records [1], financial markets [2], and cus- tomer relations management [3]. However, accessing relational databases requires an understanding of query languages such as SQL, which, while powerful, is difficult to master for non-technical users. Even for an expert, writing SQL queries can be chal- lenging, as it requires knowing the exact schema of the database and the roles of various entities in the query. Hence, researches has recently appeared to approach systems that map natural language to SQL query, and a long-standing goal has been to allow users to interact with the database through natural language [4,5]. We refer to this task as Text-to-SQL.

In this work, we present our approach based on Classifications [6] and Recurrent Neural Networks [7], precisely on LSTM [8] and GRU [9] cells. The idea is inspired from SQLNet approach [10]; in particular, we employ a sketch to generate a SQL query from naturel language. The sketch aligns naturally to the syntactical structure of a SQL query; Neural Networks are then used to predict the content for each slot in the sketch. Our approach can be viewed as a neural network alternative to the traditional sketch based program synthesis approaches [11,12].”


Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 144)

SpringerLink provid researchers with access to millions of scientific documents from journals, books, series, protocols, reference works and proceedings.


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Chloe VEAUVY July 19, 2020 0 Comments

Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL

Discover our conference paper Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQLInternational Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7).


Thanks to the Novelis Research Team for their knowlegde and experience.



Building a natural language interface for relational databases is an important and challenging problem in natural language processing (NLP). It requires a system that can understand natural language problems and generate corresponding SQL queries. In this article, we propose the idea of ​​using type information and database content to better understand the rare entities and numbers in natural language problems to improve the model SyntaxSQLNet as the latest technology in Text-to-SQL tasks. We also showed the global architecture and technologies that can be used to implement our neural network (NN) model Text2SQLNet, and integrated our ideas, including using type information to better understand rare entities and numbers in natural language problems. If the format of the user query is incorrect, we can also use the database content to better understand the user query. The realization of this idea can further improve the performance in Text-to-SQL tasks.


About the study

Relational databases store a vast amount of today’s information and provide the foundation of applications such as medical records (Hillestad et al., 2005)[1], financial markets (Beck and al., 2000)[2], and customer relations management (Ngai et al., 2009)[3]. However, accessing relational databases requires an understanding of query languages such as SQL, which, while powerful, is difficult to master. Natural language interfaces (NLI), a research area at the intersection of natural language processing and human- computer interactions, seeks to provide means for humans to interact with computers through the use of natural language (Androutsopoulos et al., 1995)[4]. Natural language always contains ambiguities, each user can express himself in his own way.”


Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


SpringerLink provid researchers with access to millions of scientific documents from journals, books, series, protocols, reference works and proceedings.

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Chloe VEAUVY December 1, 2019 0 Comments