Applied Artificial Intelligence

Because data should work for you

Advanced mathematics and machine learning methods can use data to improve business processes. Artificial Intelligence is able to identify profitable opportunities when included in decision-making. Some other use cases of machine learning are natural language processing, cybersecurity, infrastructure management, training, you name it.

How can you use your data?

— Insights

— Pattern detection

— Automated decision making

With the help of data, you can easily gain insights and draw conclusions about your current business.

Data can also help you with tracking overall business performance through interactive dashboards by detecting patterns, trends and anomalies.

By using predictive modelling, you can automate the decision-making process, gather suggestions based on a countless number of variables or automatically assign tasks to people the most suited for them. In the end, every job Artificial Intelligence handles, leaves your employees with less workload.

AI software development process

1 —

Business goal — Defining your pain point, collecting information, and setting expectations.

2 —

Data consolidation and understanding — Exchanging information with the in-house expert, obtaining data from data sources, scrubbing data, setting data strategy.

3 —

Development — Creating magic. Developing a robust AI solution that meets your business’ needs, e.g. exploratory analysis, interactive dashboards, predictive modelling.

4 —

Testing and review — Validation of created solution on historical data. Analyzing implemented approach and returning to data understanding until a developed solution meets the requirements.

5 —

Solution — Letting you enjoy the benefits enabled by our solution delivered through Cloud or on-prem with the added bonus of customer support or periodical model retraining on request.

Tools and technology we are using

Development

Tools and languages

Python

jupyter

R

Libraries

scikit-learn

NumPy

Keras

TensorFlow

Results

Data analytics platforms

Microsoft Power BI

Deployment options

on-prem

Microsoft Azure

AWS

Google Cloud

Questions and Answers

Here is a list of questions and answers that may have popped up in your mind. If there are questions we didn’t consider, don't hesitate to contact us. 👋
Pretty simple, right? Works just like chatbots.

What does the process look like?

Every project-based on Artificial Intelligence consists of four following steps: business analysis, data pipeline, modelling and deployment.

Data firstly needs to get structured and have a defined flow. After training data is organized, tagged and labelled, it is suitable for modelling and evaluation. When a machine learning model is created, the process continues with deployment to cloud service or on-premise resources and periodical retraining. If you have some necessity for customer support after, we are there to help you.

To create a data science solution, we need to gather historical data and get to know your business goals. Knowing what you really need enables us to track the results using actual business KPIs and allows us to come up with creative ideas on how to reach your goals.

A result of the AI process is normally a model or algorithm packed in a Docker container. Docker is an open platform that enables quick and easy software delivery through Cloud or on-prem. However, if there is a way of distribution that suits you and your infrastructure better, we can easily adapt.