Artificial Intelligence

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Artificial Intelligence

our ai team are experts in applied artificial intelligence and data science

Machine Learning
To create data-driven products and make informed decisions, our consultants help design data infrastructure and our engineers implement data pipelines and Extract, Transform, Load (ETL) processes for data warehouses and applications.

Deep Learning
We provide deep neural networks for computer vision, including image and video classification, object recognition and voice recognition that can significantly outperform traditional methods. Our engineers are using cloud infrastructure and GPUs to speed up the process of training the network.

Natural Language Processing
Natural Language Processing techniques can help with text analytics, classification and generation, especially with recent advancements in transfer learning. We build chatbots, organise huge text databases, extract keywords or assess the sentiments.

Reinforcement Learning
We develop reinforcement learning methods to teach algorithms on how to perform a given task in order to maximize the rewards, which is a highly effective approach when there is not a lot of training data. Reinforcement learning techniques proved useful in areas such as gaming or robotics.

Predictive and Prescriptive
We build predictive and prescriptive analytics to help our clients better understand what is going to happen, for example through churn prediction or predictive maintenance. Our engineers combine multiple data sources to generate insights and predict future trends or events.

Exploratory Data Analysis
We use advanced analytics techniques to uncover hidden insights and generate actionable recommendations with exploratory data analysis. Our data scientists connect various data sources, generate hypotheses and validate them against data to make better decisions.

Interactive Reports and Dashboards
We leverage the latest tools and libraries for interactive reports and dashboards that help us and our clients understand the data and visualise it from different angles. The reports can provide real-time information on the business situation or highlight bottlenecks in the processes.

Data Preprocessing
We perform data audits and assessments followed by the design of data preprocessing and cleaning pipelines. This is a crucial part of every data engagement and it helps us ensure we use the right data for the given problem in the most effective way.

Unsupervised Learning
We prepare clustering algorithms to create customer segments to help understand the customer base or identify user behavior patterns. Our engineers also develop recommendation systems to personalise the content and anomaly detection solutions for fraud detection.

Robotic Process Automation
We implement robotic process automation tools that automatically handle most repetitive tasks such as text, document or email processing. Modern artificial intelligence solutions can augment and speed up labor-intensive tasks, which can significantly improve productivity.

AI Transformation
Our consultants help design and implement the AI transformation lifting our clients’ analytics maturity from descriptive statistics illustrating the past to predictive and prescriptive analytics outlining the future and introducing them into the AI-first world.

AI Strategy
We create a holistic artificial intelligence strategy starting with the use case prioritisation matrix and assessing the possible impact and feasibility of each idea. This way, we ensure our clients can reap the rewards from AI and data science quickly and effectively.

Technology Roadmap
An indispensable element of artificial intelligence strategy is a technology and algorithms roadmap. Our AI, data science and cloud experts have broad experience with analytics and cloud providers and can help select the most cost-effective solution.

Research & Development
Our AI experts with academic experience can help you with your R&D challenges with research, experimentation and evaluation of novel algorithms and techniques. Leveraging our MLOps expertise, we focus on quick hypothesis testing and iteration.

Data-Driven Products
To create data-driven products and make informed decisions, our consultants help design data infrastructure and our engineers implement data pipelines and Extract, Transform, Load (ETL) processes for data warehouses and applications.

Big Data
Given our IT experience, we build efficient big data solutions and real-time data stream processing engines responding to data workloads and clients needs. We have experience with Big Data sets from industries such as geospatial, automotive or IoT.

Machine Learning Operations
We can help you automate and manage the model development lifecycle with machine learning operations (MLOps) solutions and best practices. We bring our software delivery and DevOps experience to the machine learning field to better manage production deployments.

Production Ready
We combine AI and software development expertise to develop production-ready products. Our focus is on impact of the final solution and we do not stop on the PoC phase. We look for the most cost-effective models balancing the performance and complexity of the solution.

Artificial Intelligence Team

How we do it?
Artificial Intelligence is playing an ever more important role in business. Every year, we see a fresh batch of executives implement AI-based solutions across both products and processes. If you want to boost your business and have production-ready solutions, you need someone who knows how to do it.

  1. Business Understanding - We start with workshops and problem-solving sessions to thoroughly understand the challenge, define the scope and KPIs against which we will measure the success of the solution.
  2. Data Understanding - We identify various data sources, connect them, verify our understanding with the business unit and run exploratory data analysis to check the initial hypothesis and build up our business understanding with data-based reports.
  3. Data Preparation - We build up data pipelines that help us process and combine multiple data sources that are needed for the problem we are solving. We clean the data and prepare it in the format needed for modeling.
  4. Modeling - We develop machine learning algorithms for a specific task, optimise the parameters, verify multiple approaches or model types and train them on the data prepared in previous steps. We also take into consideration the complexity of the model.
  5. Evaluation - Based on previously defined KPIs, we evaluate the model’s performance, run error analysis and identify areas for improvement and refinement of the model. This also helps us to better prioritise our work.
  6. Deployment & Monitoring - Once we select the final model, we deploy it in production and monitor the results. It is important to verify the results over time to make sure that the distribution of the input data and results do not shift.

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