Development Framework

Full Software Development Life-Cycle fosters transparency and trust throughout the implementation process.

Leader in Intelligent Automation and Business Process Strategy

We believe the Transformation of work is inevitable, but the traditional ways of progress and development are insufficient to realize true organizational change. 

Our development team is fully certified in a wide-range of Intelligent Automation technologies, such as Power Automate, Zappier, UiPath, Automation Anywhere, CoreAI, Appian, Outsystems, Nintex, ChatGPT and more.

STATISTICS & IMPACTS

Top AI & automation statistics facts and figures for 2023

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Of enterprises are expected to experiment with AI technologies

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Of average ROI is realized by organizations that adopt RPA

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AI is expected to have a positive net impact on job creation, leading to the creation of 2.1 million jobs

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We create optimized solutions by integrating technologies
to solve any business challenge.

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Train your data to be intelligent & actionable

We offers a variety of professional technical services that
leverage Artificial Intelligence (AI), Robotics Process Automation (RPA), Data Analysis and Machine Learning.

We create optimized solutions by integrating technologies to solve any business challenge.

Curious to learn more?

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Train your data to be intelligent & actionable

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Frequently asked questions

Robotic process automation (RPA) occurs when rule-based tasks are automated via software robots that can function across a variety of applications, just as human workers do. A bot can do almost anything a human can do. Bots cannot replace humans; they just allow humans to concentrate on more strategic tasks.

Robotic Process Automation (RPA) is a technology that automates repetitive, rule-based tasks within business processes. Here’s how it works:

  1. Identify Tasks: First, you identify the tasks or processes that are repetitive and rule-based, such as data entry, form filling, or data extraction.

  2. Create Automation: RPA software, or “bots,” are then configured to mimic human actions. These bots can interact with applications, systems, and websites just like a human user.

  3. Training: The bots are trained to perform these tasks by recording the steps or providing instructions. No complex coding is required.

  4. Execution: Once trained, the bots can execute these tasks automatically and repeatedly, with high accuracy and speed.

  5. Integration: RPA can work with existing systems and applications, making it versatile and easily adaptable to various industries.

  6. Monitoring: The RPA system is typically monitored to ensure tasks are completed correctly. Exceptions or errors are flagged for human intervention.

RPA is particularly effective in streamlining processes, reducing human errors, and saving time and costs in repetitive, manual tasks.

Return-on-investment (ROI) depends on the bot’s complexity however returns are realized typically within ~9 months via time and labor saved executing the task. Bots are reliable and accurate, they don’t get sick, don’t quit, and don’t call off sick. Bots also maximize use the infrastructure, drives innovation by freeing up employees to focus on high value-added activities and increase the employee’s Quality of Life.

RPA programming takes advantage of using Low-code or No-Code to develop bots. Low or No code allows people of all skill levels to quickly design applications with little need to sling code via command line as low/no code allows for the dragging and dropping of visual blocks of existing code into a workflow to create bots.

AI, or Artificial Intelligence, is a field of computer science focused on creating systems that can perform tasks requiring human intelligence, such as learning, problem-solving, and decision-making. It involves the development of algorithms and models that enable computers to mimic human cognitive functions.

AI works by using algorithms and data to simulate human intelligence. It involves several key components:

  1. Data Collection: AI systems require large amounts of data to learn and make predictions or decisions.

  2. Training: Machine learning models are trained on this data to identify patterns and relationships.

  3. Algorithms: These are sets of instructions that enable AI systems to process and analyze data.

  4. Inference: Once trained, AI systems use these algorithms to make predictions or decisions based on new data.

  5. Feedback Loop: Continuous learning and improvement occur through feedback and new data, enhancing AI’s performance over time.

AI techniques vary, including machine learning, deep learning, natural language processing, and computer vision, each designed for specific applications.

The time to achieve ROI with AI typically varies. For instance, a simple chatbot might take a few months to start saving customer service costs, while more complex AI projects, like predictive maintenance in manufacturing, can take a year or longer to show significant savings. It depends on the project’s complexity and data quality. Planning and patience are key.

Yes, specialized skills are often required to automate tasks or decision-making using AI. These skills include expertise in machine learning, data science, programming languages like Python, and knowledge of AI tools and frameworks. Additionally, domain-specific knowledge can be crucial for tailoring AI solutions to specific industries or tasks. Collaboration among data scientists, domain experts, and software developers is often essential for successful AI automation.