Artificial Intelligence

Our AI -Objectives

  • Firstly, help analyze business pain points; conduct interviews with stakeholders, design and develop solutions.
  • Secondly envision and architect solutions involving for example address today and tomorrow’s business problems.
  • Thirdly we build machine learning models from scratch, most importantly both POC and production stage


What are AI and ML? – Key Terms and Concepts

  • Artificial Intelligence (AI) — A category of technological pursuits that hopes to replicate how humans think — that is, what makes them intelligent.
  • Machine Learning (ML) — A branch of probabilistic computational techniques that has yielded much of the recent progress towards Artificial Intelligence in the last decade.
  • You will usually hear ML and AI used interchangeably in the business world.

To sum up, the concept of Artificial intelligence is wide and covers many areas. 

  1. Data science
  2. Machine learning
  3. Deep learning
  4. Neural network
  5. Object detection
  6. Computer vision
  7. Face recognition

As a result, progress has reached such a level that technologies based on artificial intelligence are used in completely different areas of business.

For example one of the main tasks of AI remains business automation.

We work with customers to:

  1. Dive deep into data
  2. Doing analysis
  3. Providing guidelines
  4. Building strategies
  5. Discovering root causes
  6. Designing long-term solutions

Above all we deliver AI/ML/DL project from beginning to end, meanwhile understanding business requirements. We develop architecture, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact.

In conclusion we launch data-driven business by creating frameworks, designing target processes, identify organizational structures.

See our AI Dashboards Page

Azure Machine Learning
Artificial Intelligence

Industry use cases of AI in Healthcare

Increasing Healthcare Team Capabilityby Task

  • Recommend appropriate imaging modalities for common clinical applications of 2D medical imaging
  • Perform exploratory data analysis (EDA) on 2D medical imaging data to inform model training and
    explain model performance
  • Establish the appropriate ‘ground truth’ methodologies for training algorithms to label medical images
  • Extract images from a DICOM dataset
  • Train common CNN architectures to classify 2D medical images

Increasing Healthcare Team Capability-by Functional Area

  • Applying AI to EHR Data
  • 2D Medical Imaging Data
  • 3D Medical Imaging Data
  • Applying AI to Wearable Device Data