Data Robotics

Data Driven Machine Learning Robots
Efficient and flexible business processes

Data Robotics

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The Data Robotics

Data Robotics (Data-driven machine learning Robots), 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, with the aim of increasing the productivity and efficiency of business processes, referring to the consolidation of robotics and Machine Learning techniques, facilitates the introduction and integration of automation into organisational processes.

Certain operations, immediate in nature for the operator concerned, can be carried out efficiently by a robot with the support of Machine Learning techniques. This synthesis has clear application for many business processes across all industries.

In particular, the Data Robotics framework includes both Robotic Process Automation (RPA), as well as Intelligent Process Automation (IPA) tools. Thanks to the application of “smart” technologies IPA guarantees an improvement of RPA, facilitating the evolution from solutions that handle straightforward and recurring tasks, to new paradigms based on machine learning techniques. This allows Data Robots to develop new knowledge, to make decisions, to make assessments and to provide feedback: “it takes the robot out of the human”.


Reply supports its customers from the very beginning of the automation path through the application of the “Automation Opportunity Matrix”, a structured method, developed in collaboration with DEVO Lab by SDA Bocconi, which allows the mapping, based on qualitative and quantitative features, of internal processes in four dimensions of analysis: the structure of business processes; the importance of the process for carrying out the company's business; the economic value of the process and the exposure of the company to risks if the process fails to meet certain operating and / or regulatory standards.


Multiple decision making

Multiple data sources
Learning based on statistics
Natural language recognition
Meaning comprehension

Pattern based decisions

Learning based on pattern recognition
Non-structured data
Autonomous learning with human in the loop
Limited decision-making based on provided information

Structured Rules

Structured data
No decision-making

Basic Automation / Workflow

Single / Macro application

Machine Learning
Realizing Data Robotics solutions means working in this scope


Data Robot is the result of the training process based on:

  • Historical data
  • Continuous refinements of operator / user historical outcome of the taken decisions

Historical Data Gathering

The Data Robot "observes" for a certain period of time the input data and the decision taken by the human user.

Algorithm Competition

The 70% of input data and respective decisions caters the Data Robot: the algorithm that best fits the decisions taken by the human is chosen.

Test Accuracy

The remaining 30% of input data are used by the chosen algorithm to take the decisions; if the decisions match the ones taken by the human the algorithm is confirmed, else the input data are coupled with the correct decisions and used to refine the Data Robot algorithm.

Continuous Learning

Once the initial training is completed, the new input data are processed by the Data Robot algorithm that take the decision autonomously and assign a "level of confidence": if too low, the Data Robot asks the human to confirm / modify the decision. In case the Data Robot decision is discarded, it will be used to refine the algorithm as described in the "Test accuracy" step.


Improved Data Analytics

The more data you have the better decision you can take on a micro and macro level.
The more processes are traced the more you can get opportunity to identify optimization gaps and increase efficiency.

Increased Regulatory Compliance

To automate means to fully track and document the system automated.
Data Robotics solution provides in depth telemetry about workflow, enabling deep insight to comply with specific regulations.

Increased Efficiency

Data Robotics solution never needs time off (24/7).
The same volume of work can be done in less time.
Downstream work commences sooner.

Higher Employee Productivity

While Data Robotics handle the more repetitive jobs, employees can participate in more value-added activities (personal interaction, problem solving, decision making).
When employees feel their work is valued and worthwhile, their productivity increases.
In addition employees are better supported for their value-added tasks, increasing productivity again.

Improved accuracy

Employees are human and all humans make mistakes.
Data Robotics eliminates processing errors if all processes and sub-processes are well mapped.
There will still be need for testing, training and governance of the Data Robot.

Logistical upside

Complication with offshore labor are minimized or eliminated (time zone differences, cultural and language barriers, …).
Decrease the need for employee recruitment and training costs.

Flexibility and Scalability

Remote management of IT infrastructure to investigate and solve problems for faster process throughput.
Data Robotics makes it easy to maintain a scalable infrastructure, allowing to handle short-term demand without extra-recruiting or training.


Reply has developed a number of accelerators aimed at applying Data Robotics to different use cases.

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    Accelerator for end-to-end management of the invoicing process, from data entry automation for information contained in invoices, to the automated verification and reconciliation, using OCR and Machine Learning algorithms, of the invoices and the relative delivery notes, with automated identification of the reasons for any discrepancies (i.e. prices and/or quantities different from those agreed upon, etc.), based on product codes in the materials database and any other relevant data.


    Accelerator for the automated creation of employee expense reports through the capture of receipts using smartphone cameras which are sent to a dedicated inbox in order to categorise the expenditures, to carry out reconciliation controls on the expense report submitted and to handle the fully automated approval phase.

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    Chatbot capable of retrieving all the necessary information from multiple business systems, obtaining real-time updates on the status of orders received from a customer in order to analyse key KPIs or to request customer information; the bot can also be activated by implementing components focused on the availability of goods in stock, promised delivery dates, delivery status and claims management.


    Match-up is an advanced tool for the analysis, reconciliation and matching of complex data (single and/or multiple). Match-up is the value added solution in terms of: ease of use; independence from platforms; flexibility of the usage model (cloud-based or residing on the company’s servers); information enrichment to support reconciliations; alternative actions or proposals (reconciliation proposals or recommendations reports); taxonomy of rules (simple, medium and complex) and data robotics.

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    Claim Prediction in TELCOs

    The Claim Prediction Tool (ClaP) is a system for the analysis and supervision of customers at risk of churning. The use of this tool finds application and generates value within the general framework of CRM and Customer Retention activities, with particular reference to Proactive Caring, to actively monitor and prevent reports of customer dissatisfaction and Trouble Ticket Management, to effectively solve high-priority issues, assigned based on customer profiling.


Are you looking to start your first IPA Project? Download now our IPA Checklist to understand what to take care of and how to avoid major pitfalls to kick-start your IPA project right away.