RPA & NLP

Use of Robotic Process Automation and Natural Language Processing has enabled a number of scenarios where a lot of repetitive tasks as well scheduled tasks, currently managed by manual labours, can be easily orchestrated and automated using frameworks such as UiPath, Automation Anywhere, Blueprism, and front ended through virtual assistant or chatbots, using NLP engines such as IBM Watson, Google Dialogflow, AWS and Azure.

Some of the avenues, where we have applied RPA has been in the avenues of:

Bill of Materials

  • RPA automate BOM the list of raw materials, components, sub tasks required for the production processes .
  • The main functions require information like Raw produces, components , assembling many more information to support the on-going activities in a manufacturing
  • RPA helps in delays, errors in the production cycle which will be automatically recorded and rectified to have negative effect on the business which can lead to huge losses to a company .
  • RPA which decreases the involvement of Humans In the process and automate the entire process which can be repetitive with more accuracy which results in fewer defects and on time process completion

Administrative Audit and Reporting

  • RPA helps in providing solutions where the auto invoices can be generated for different without Human Intervention which results in accuracy and effective decision making
  • RPA Can also automatically generate invoices and reports and send it to management and customers

Invoice Process Automation

  • Invoice processing using machine learning, computer vision, and OCR
  • RPA with machine learning capabilities can read and understand data from unstructured data sets
  • RPA with advanced OCR and computer vision capabilities read and fetch data from image-based invoices


Inventory Management Process Automation

  • RPA helps in Time intensive and sensitive Business processes to be automated
  • RPA automates the Storing, procurement process used for production
  • RPA helps in continuous inventory monitoring
  • RPA helps inventory flow from the moment it reaches your warehouse, gets put into the production line to shipping.


NLP coupled with RPA, brings a whole new dimension to agility, business transformation and human less / autonomous economy.

Today’s enterprise relies on Agile processes, lean methodologies and unique avenues to automate lowering the overhead costs without compromising on the quality.

With the advent of the emerging technology advancements such as CNN, RNN and other Deep Learning libraries, it is possible to make a machine to think like human beings, to interact like human beings and to also take decisions like human beings.

In a classic scenario of today’s manufacturing industry, ecosystems lack connected ness, not every factory has smart devices or infrastructure in place, to start getting data out of machines, etc.

This can lead to unplanned outages, non-replenished stock inventories, delays in procurement, leading to assembly line delays and other relevant burning avenues, that automatically leads to a lowered Customer Satisfaction.

All of this due to lack of data transparency, below par customer service and leads to loss of revenue. Also, with growing man power costs and other cost overheads, most enterprises are looking to automate some of their process workflows thro’ the incumbency and convergence of IoT and AI.

Imagine creating a service agent that can interact thro’ speech with customers, and is a connected system overall which is always “live data” ready? It can easily take count of live stock inventories thro’ IoT Systems and translate the data into a voice output making customer experience human like. Similarly, a Service Request registered can be automatically pushed into Replenishment Cycle for timely action and faster turnarounds, etc.

Some of the fast-growing areas of AI adoption comes in the area of automating Customer Service, having a Digital Agent creating meeting requests with Potential Prospects, discoverng leads for Augmenting Inside Sales Teams, etc.

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