Overview

IntelliBuddies is a full-stack RPA platform. On top of activities provided to automate repeated rule-based tasks, IntelliBuddies also provides AI/ML Activities so that you can infuse AI into your automation, thus enabling your buddies to think, decide, and learn like humans. It includes many activities under the Machine Learning category, as listed below.

Activity	Description
Classification Model Generator	Generates a Classification Model with the specified name using the specified dataset and algorithm.
Classification Model Predictor	Invokes the specified Classification Model on the input data and returns the response from the model.
Clustering Model Generator	Generates a Clustering Model with the specified name using the specified dataset and the specified algorithm.
Clustering Model Predictor	Invokes the specified Clustering Model on the input data and returns back the response from the model.
Extract Meta Data	Extracts the metadata from the content provided based on the specified Content Type.
Match Mail Type	Matches the email type with the specified type based on the email content provided. It returns true or false based on the match result.
Number Parser	Parses the string expected to hold the Numeric value and give you back the good Numeric value if there are any parse errors.
Regression Model Generator	Generates a Regression Model with the specified name using the specified dataset and algorithm.
Regression Model Predictor	Invokes the specified Regression Model on the input data and returns the response from the model.
Date Parser	Parses the string expected to hold the Date value and give you back the proper Date value if there were any parse errors.
Detect Language	Detects the Language of the provided text using IntelliBuddies AI Cognitive Services.
Detect Objects	Detects the objects inside the given image using IntelliBuddies AI Cognitive Services.
Document Classifier	Predicts the type of document based on its content.
Get Key Phrases	Extracts the key phrases of the provided text using IntelliBuddies AI Cognitive Services.
Get Named Entities	Detects named entities of the provided text using IntelliBuddies AI Cognitive Services.
Get PII Entities	Detects the provided text's Personally Identifiable Information(PII) entities using IntelliBuddies AI Cognitive Services.
Get Text Summary	Generates the summary of the provided text using IntelliBuddies AI Cognitive Services.
Invoice Analyzer	Analyzes and extracts all the metadata from the specified Invoice.
Number Parser	Parses the string expected to hold the Numeric value and give you back the good Numeric value if there are any parse errors.
Sentiment Classifier	Detects the specified text's sentiment as positive, negative, or neutral.
Speech To Text	It transcribes the provided audio into text using IntelliBuddies AI Cognitive Services.
Text To Speech	It converts text to audio and saves it to a specified file using IntelliBuddies AI Cognitive Services.
Translate Language	Translates the Language of the provided text using IntelliBuddies AI Cognitive Services.

Python Context

Our Machine Learning Activities need a Python environment to execute. Hence, it is mandatory to have all our Machine Learning Activities to be embedded inside Python Context activity. The Python Context container activity connects to the Python environment installed on the execution system. It enables the execution of the activities within this container using the Python environment.

PythonObject

For automation to pass and retrieve data to and from Python Context, IntelliBuddies has introduced the PythonObject data type. You can use PythonObject to handle and manipulate Python data. You can convert PythonObject into its corresponding .Net data type using ConvertPythonObject activity.

Example

To understand AI Infused Automation using IntelliBuddies, we have created this example to understand better our Machine Learning Activities.

Download Example

The example has two processes.

InsurancePremiumPredictionModelGenerator

This process will generate a Regression Model using the Random Forest ML algorithm. Generates the model based on the insurance dataset "insurance.csv." Refer: insurance-data

InsurancePremiumPredictor

This process tests the model generated by the previous step. This process accepts input data from the user for all the required columns. It then invokes the generated model, passing the input data received. It shows the predicted premium by the model in MessageBox.