Intelligent Automation

Eutopia Systems, with a vision of touching 1% of global businesses and helping them transform their businesses by harnessing the power of Software Robotics, Cognitive Computing, Artificial Intelligence and Analytics; focuses on Intelligent Automation and Data Engineering & Business Analytics.

Our offerings in the area of Intelligent Automation include:

Robotics Process Automation (RPA)

Robotic Process Automation (RPA) is the automation of back and front office processes that are largely rules based, structured, and repetitive. The automation takes place when software “robots” (not physical robots) carry out processes or tasks normally completed by humans.

Process automation software has been around since the turn of the century, but it has recently had a positive injection with a new label added to its name: “robotic”. This name change has modernized and linked the business case for RPA to head-count savings. The software vendors of RPA have positioned their software as a headcount saving and have priced their software on the higher end of software, but on the lowest end of hiring a human employee.

The adoption rate of RPA has been gaining momentum over the past two to three years largely due to the increasing attention it has received from tier one consulting firms, and from the increase in IT vendors providing RPA solutions and delivering more sophisticated software.

At Eutopia Systems, we work with organizations to build automated solutions that help them navigate data touch points that are unstructured in nature and then helping them mature towards intelligent systems that make optimal decisions while learning continuously from their environment. Our end to end RPA services enable our clients to understand current automation levels and discover opportunities for reducing operational cost. We help organizations to:

  • Select most appropriate RPA governance model, change management plan, and deployment strategy.
  • Assess and prioritize automation opportunities and channel efforts according to current automation rates, transactional volume, and ease of implementation.
  • Build commercial business case and verify its value during a first RPA proof-of-concept to secure management buy-in and align key stakeholders.
  • Implement and develop services to create automation ranging from simple screen capture to virtual chat bots to highly intelligent cognitive robots.

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RPA for SAP

SAP business processes are the front line of your business—streamlining everything from manufacturing and service, to sales and finance. However, they typically extend beyond SAP to other systems which may reside on-premise, in the cloud or in hybrid environments. The complexity of this IT landscape creates processing errors and delays that frustrate customers; application silos limit your visibility and control; while your staff are tied up managing processes and checking for errors. So how do you achieve SAP acceleration?

To maximize your investment in SAP software you need to automate your SAP and non-SAP systems end to end. In doing so, you will regain control, dramatically shorten processing times, reduce costs and make all of those last minute scrambles to meet deadlines a thing of the past. The key capabilities to achieve these benefits are:

  • Visibility and control of the end-to-end process
  • Full coordination of every step of the process across both SAP and non-SAP systems
  • Integration of file transfers into the process
  • Processing of data in parallel
  • Only trusting your most critical processes to a solution that has been certified by SAP

Whilst there are many other factors that are also important to consider, only by ensuring that your solution encompasses the above key capabilities as a foundation can you be certain that you will be able to meet the ever-increasing demands of the business

The general characteristics of a SAP process which is ready for robotic process automation would be:

  • repetitive and rules based
  • accesses structured data sets
  • utilizes applications on a Windows or Web based platform
  • the process is documented and has been standardized in practice
  • three or more staff are hired to complete the process
  • manual data input, prone to human error

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Cognitive Automation & Artificial Intelligence

Cognitive automation is based on software bringing intelligence to information-intensive processes. It is commonly associated with Robotic Process Automation (RPA) as the conjunction between Artificial Intelligence (AI) and Cognitive Computing. By leveraging Artificial Intelligence technologies, cognitive automation extends and improves the range of actions that are typically correlated with RPA, providing advantages for cost savings and customer satisfaction as well as more benefits in terms of accuracy in complex business processes that involve the use of unstructured information. Cognitive automation leverages different algorithms and technology approaches such as natural language processing, text analytics and data mining, semantic technology and machine learning.

How do Companies use Cognitive Automation?

  • Identifying specific products or objects within an image
  • Extracting and matching relevant data from unstructured documents
  • Synthesizing large volumes of information into concise descriptions

Organizations can realize costs savings through the effective use of cognitive and robotic process automation. Other potential benefits—from improved flexibility to higher employee morale—can extend the value of cognitive automation:

  • Decreased cycle times and improved throughput: Software robots are designed to perform tasks faster than a person can and do not require sleep, making 24×7 operations possible.
  • Flexibility and scalability: Once a process has been defined as a series of instructions that a software robot can execute, it can be scheduled for a particular time, and as many robots as required can be quickly deployed to perform it.
  • Improved accuracy: Robots are programmed to follow rules and do not make typos.
  • Improved employee morale: The tasks and processes most suitable for automation are typically the most onerous and least enjoyed by employees. Employees relieved of these tasks can focus on more important and rewarding work.
  • Detailed data capture: The tasks performed by machines can be monitored and recorded at every step, producing valuable data and an audit trail that can support further process improvement and help with regulatory compliance.

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Service Delivery Automation with DevOps

“DevOps” as a term was first coined in 2009 by Patrick Debois, who became one of its chief proponents. Simply put, DevOps is a combination of software development and operations—and as its name suggests, it’s a melding of these two disciplines to emphasize communication, collaboration, and cohesion between the traditionally separate developer and IT operations teams.

Rather than seeing these as two distinct groups who are responsible for their specific tasks but don’t really work together, the DevOps methodology recognizes the interdependence of the two groups. By integrating these functions as one team or department, DevOps helps an organization deploy software more frequently, while maintaining service stability and gaining the speed necessary for more innovation.

At its essence, DevOps is a culture, a movement, a philosophy.

It’s a firm handshake between development and operations that emphasizes a shift in mindset, better collaboration, and tighter integration. It unites agile, continuous delivery, automation, and much more, to help development and operations teams be more efficient, innovate faster, and deliver higher value to businesses and customers.

What Challenges DevOps Solves?

Prior to DevOps application development, teams were in charge of gathering business requirements for a software program and writing code. Then a separate QA team tests the program in an isolated development environment, if requirements were met, and releases the code for operations to deploy. The deployment teams are further fragmented into siloed groups like networking and database. Each time a software program is “thrown over the wall” to an independent team it adds bottlenecks.

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