Bill Factotum

Bill Factotum is an intelligent tool that allows you to use Artificial Intelligence to derive information of projects and commissions more simply and intuitively.

Bill Factotum connects to the Project Management software and allows for natural language interaction thanks to the identification of meta tags in the dataset.

Bill Factotum is based on the conception that the added value given by the end user is about data interpretation rather than the creation of reports using complex User Interfaces (UIs).

Furthermore, Bill Factotum comes with an advanced neural network that allows it to make previsions that are more accurate than the ones made by the Project Management Software using traditional statistical tools. It is possible to use these forecasts in two ways:
– Asking them directly to Bill: “If I assigned two more developers to project Alpha, which impact would it have on costs and delivery date?”
– Using a proactivity function, which allows Bill to autonomously conduct simulations on data and identify potential opportunities and risks: “Given the actual conditions, project Beta has a 92% chance of not being completed by the deadline. To lower the chances under the 20% threshold, it is necessary to add four full-time developers to the staff, with an increase in costs of 12%.”

What it does

Understand natural language to interact with the company’s data
Bill Factotum is capable of understanding natural language and convert it into an input to communicate with the Project Management software. The use of natural language as a mean of interaction removes the learning curve barrier, thus saving significant amounts of time and having the user focus only on the activities that create value.

Creation of forecasts about future scenarios using “White Box” Neural Networks and causal correlation
Bill Factotum can create future scenario analyses thanks to the AI networks developed by Divisible Global, more potent than the traditional prevision systems used by Project Management software.
Divisible’s causal correlation algorithm is capable of identifying cause-effect relations within data (and not only mathematical ones, as happens with statistical correlation), while the White Box neural network provides a detailed overview of the most relevant variables that affect the end result.

Using proactivity to autonomously produce scenario simulations to identify potential opportunities and risks
Bill Factotum can autonomously simulate scenarios to immediately identify opportunities and risks that may happen thanks to its Proactivity function, all with settable custom goals that can act as guidelines for Bill’s analyses on the goodness of scenarios. For example, if we want to deliver all our projects by their respective deadlines, Bill determines the optimal re-allocation of resources with the eventual increase in costs.

Supervised and Unsupervised Learning
Bill Factotum can learn to improve its predictive model as time passes, by comparing old forecasted data and actual data (supervised learning) and analyzing new data (unsupervised learning).

Customization of models through Hornet AI
Bill Factotum is capable of developing predictive models that answer to the primary needs of a company and is the result of years of field studies and experiences. But, since we know that every firm has different needs and preferences, we developed an integration with our Hornet AI technology for Bill Factotum that allows for the independent creation of machine learning models to answer to all the question that a company may have.

Data Storytelling: Optimized report creation
Bill Factotum can help the user in transforming the multitude of data into useful information to take effective decisions – one of the hardest challenges nowadays. Bill Factotum can recognize data types and guide the user in the creation of relevant and trustworthy reports.