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We create a highly cost-efficient and productivity-efficient digital workforce. We develop solutions with our own and partner technologies for the digitization and automation of business processes.
RPA (Process Automation) solutions are effective for business of all sizes. Small, medium and large enterprises can apply RPA in several areas, such as finance, accounting, sales, service, human resources, among others.
Smart Automation allows companies to assign robots (bots) to repetitive functions that require the attention of the operational team and take precious time away from employees. The service creates the opportunity to transition your workforce to a digital model with very short implementation times. Bots operate uninterruptedly 24/7 and integrate with the ecosystem of your business, optimizing time, decreasing the volume of activities assigned to the team and, consequently, reducing cost.
How it works
Like humans, robots use the user interface to capture, interpret and handle data in a matter of seconds. This allows for effective communication with several other systems and the possibility for customized deployment in virtually any industry, regardless of the complexity or volume of the operation.
A study by Capgemini Consulting, Technology, Outsourcing suggests that AI (Artificial Intelligence) can generate 10-25% cost savings, with a potential for 30-50%.
“RPA” stands for Robotic Process Automation, a tool that automates routine tasks quickly and cuts costs. A bot (scheduled task director) can assist you in data capture decisions or initiatives. We can create, test, and implement new automation schemes in a few hours, instead of days or months. We can eliminate data entry errors across multiple systems. Previously manned tasks can now be seamlessly automated in seconds or minutes by bots, optimizing the scope of work. Gartner estimates that 72% of organizations will implement RPA within the next two years. In its annual survey, Deloitte states that 53% of companies have already launched their RPA initiatives, estimating that this rate will increase to over 70% in the next couple of years, and RPA will reach a virtually 100% adoption rate across all companies by 2023; RFS Research estimates $ 1 billion spent with RPA software and greater relevance in integration and cognitive abilities; Forrester estimates that the RPA market will be valued at $ 12 billion by 2023.
A term used to refer to communication robots, the chatbot acts as a digital attendant with total efficiency 24h/7d, and solves scheduling issues, process consultations and qualifications, among other features. Through the significant volume in using messaging applications, we note that the user is increasingly looking for solutions like these, available to companies of all sizes and segments.
OCR – Document scanning
Through Optical Character Recognition (OCR) technologies, we are able to extract information from unstructured data, i.e. documents and images and not spreadsheets. This information is classified and used for various processes, such as understanding clauses in contracts or validating the drivers’ license photo.
Computer Vision: Let AI show you what your eyes can’t see. We develop solutions using computer vision for facial recognition, traffic situations and identification of patterns that cannot be detected by the human eye.
We created a bot capable of making complex decisions. Traditional RPA can only make configured decisions. Cognitive bots can be trained with more complicated information and experiences, increasing the ability to automate complex processes.
Smart IT Operation (AIOPS)
Optimization of IT operations using Artificial Intelligence and automatic resolution throughout the lifecycle of operations: releases, service, incident resolution and reports.
By creating a CoE, companies are able to launch their automation programs and achieve significant indexes of corporate processes. Developing Solutions with Augmented Intelligence:
Defining the Problem: What problem are you trying to solve? What are the main impacts of the problem?
Predictions/Recommendations: What predictions or recommendations are being made? Identify prediction variables (X) and objectives (Y)
Data Acquisition: What data sources are available? Is there enough data? Can it be used?
Modeling: What are the appropriate models according to the predictions/recommendations made?
Model Assessment: How will model performance be evaluated? Define the success criteria.
Data Preparation: What transformations will be required to tailor the available data to run the chosen models and achieve the expected outcome?
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