AI & Machine Learning Development

Create applications that are powered by intelligent AI by utilizing machine learning tools and algorithms.

Why Work with Us

Artificial Intelligence and Data Science project methodology is significantly different from traditional research for software delivery projects.

It requires companies to:

Develop new data science and AI skills (such as NLP, computer vision, machine learning, deep learning, etc.). Build new infrastructure for big data and model deployment (often cloud based). Adopt new culture of collaboration between the business and data scientists.

Digital Errors can help to bootstrap AI capabilities, or fill data and analytics gaps for companies that do not have the expertise internally or do not want to hire new talent until the benefits of AI are proven.

Digital Errors focuses not only on research, but also on delivering end-to-end solutions starting with solution design and ending with deployment of ML-model and integration into the existing or newly developed client environment.

Our Services

Predictive and Recommendation Systems

 Automate the decision-making routine and forecast events with probabilistic analysis, and user personalization

Natural Language Processing

• Advanced texts, speech, and cognitive analytics
• Structured and unstructured data
• Chatbots

Computer Vision

Visual classification of object nature, image recognition, and real-time video processing

Data Mining and Analytics

Advanced data analytics, clustering, pattern detection, statistical analysis, and data visualization.



Our main value is to deliver valuable and cost-effective solutions to our clients. That’s why we developed an approach to R&D projects that allows us to see the progress at every stage and deliver solutions incrementally, allowing clients to decide if additional efforts are worth investment or a change of direction is required.



1 . Business Understanding

2 .Data Acquisition & Understanding

• Building data pipeline
• Setting up environment
• Data wrangling, exploration & cleansing

3 . Modeling

• Feature engineering
• Model training
• Model evaluation

4 . Deployment

• Scoring
• Performance
• Monitoring
• Support