Applied AI

We use Machine Learning techniques to add intelligence to the applications we build. These AI services are seamlessly integrated into the software platform with top performance, state-of-the-art architecture and methodologies, with improved security and privacy. We have leveraged applied AI in several use cases such as:

Recommender systems for personalized recommendations
Natural language processing (NLP) for document analysis and classification
Chatbots for conversational user interfaces
Image recognition for object identification
Predictions from historical data

Is your project suitable for AI?

Answer these questions to find out

Task to automate
Can you describe the task?
The task that needs to be automated, does it
take a long time to process?
Is it a repetitive task?
Are correct outcomes clear and measurable?
Collected data
Do you have reliable/objective/complete data
Can you provide data-gathering strategy
Do you have domain knowledge for the data?

Affirmative answers to above questions or curious about AI?

Let's talk and see how our platform can fit your needs.
Explore real-life applied AI solutions and discuss your needs.
Explore real-life applied AI solutions and discuss your needs.

Call us and have a chat
with our specialists

Phone call, web meeting… whenever it suits you.

A typical Machine Learning process

Real-world ML systems are composed by several components; our platform will be responsible, among others, for serving the infrastructure, extracting features, monitoring and analysis. The ML code will be a component or service integrated in the platform.

These are the most common steps of ML projects:

Problem analysis
Data preparation
Designing and building
the solution
Support and
Data analysis
Data modelling
Deployment of
the solution

We use different techniques in our AI projects

Statistics and data mining

We provide statistical analysis on large-scale databases to extract data patterns and knowledge.

Machine learning

We teach computers to perform tasks without rules or domain specific knowledge. For this purpose we have used supervised and unsupervised techniques, for classification and regression problems for the former, and clustering (similarity) for the latter.

Using reinforcement learning our software learns from trial-and-error for well defined problems.

Deep learning

We use this approach to solve intuitive problems like segmenting an image or understanding text. By gathering experience from data and using state-of-the-art deep neural networks architectures trained on GPUs, our software is able to do tasks like predictions.
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