Data, AI & ML engineering

We design and develop business-driven IT solutions

We help you leverage your data to gain market insights, automate processes, and innovate. Data analysis has become a necessary practice for companies willing to manage competition and expand businesses. Through data, businesses can indeed read and interpret current trends and intentions, make forecasts, and create higher quality insights, allowing for better decision making.

Accelerating & Simplify AI Management

Over the years, Bitrock has developed in-depth expertise on AI-related topics, with a particular focus on AI industrialisation.

The development of proprietary platforms designed to accelerate and simplify the model operationalisation and the management of the entire algorithms lifecycle has provided the opportunity to work on the lowest layer of software.

Finally, the experience gained in dozens of projects carried out in the enterprise environment for large groups helps to guarantee the quality of delivery and governance capacity of complex projects.

The synergy of these competencies positions Bitrock as a unique partner, offering unparalleled value to our clients.

OUR APPROACH

Bitrock delivers a unique approach to AI application development combining a consulting approach with unparalleled skills and expertise on one hand and the use of proprietary MLOps & AI Observability solutions that can significantly accelerate time to market and simplify AI related operations.

In particular, the following capabilities and skills are available within the Group:

Data Engineering: Data Architecture & Platform Design, Data Governance, Data Lineage
Data Science, AI/ML Engineering: Model Lifecycle Management, Data Modeling, ML Training and Engineering
AI Infrastructure: ML Ops, AI Observability at Scale, Data Quality
AI Solutions: LLM’s Tuning, RAG, KAG, Agentic AI

Vision & design Principles

Artificial Intelligence & Machine Learning

Artificial Intelligence (AI) & Machine Learning (ML) have witnessed a tremendous leap forward in recent years, mainly due to the increased availability of computing resources (faster GPUs, bigger memories) and data. Artificial intelligence has reached or surpassed human-level performances in many complex tasks: autonomous driving is now a reality and social networks use ML profusely to detect harmful content and target advertisements, while generative networks such as OpenAI’s GPT-3 or Google’s Imagen could be game changers in the quest toward Artificial General Intelligence (AGI).

AI/ML is no longer the future to look at, it’s the present.

There is growing recognition for this architecture, which is supported by a wide range of vendors, including Databricks AWS, Google Cloud, Starburst, and Dremio – and by data warehouses vendors like Snowflake too.

Multimodal Data Processing Architecture

We focus on a Multimodal data processing architecture, specialized in AI/ML and operational use-cases, able to support analytical needs typical of data warehouses.

This pattern is an alternative to a Business Intelligence oriented alternative, based on data warehouses. At the core of the system there are the concepts of data lake and data lakehouse.

Cloud first

Cloud first means prioritizing cloud over on-premise solutions. In other words, having to justify picking on-premise solutions rather than making a case for cloud ones. Despite some companies being reluctant towards cloud solutions, the advantages provided by the cloud are too many to make a case: faster time to market, easy scaling, no upfront license/hardware costs, lower operative cost. Basically, it allows us to outsource non-core processes and focus on what matters the most to the business.

Data Lake and Data Lakehouse

A data lake is a centralized repository that allows you to store and manage all your structured and unstructured data at any scale. They are traditionally oriented towards advanced data processing of operational data and ML/AI.

The data lakehouse concept adds to them a robust storage layer paired with a processing engine (spark, presto, …) to enhance it with data-warehousing capabilities, making data lakes suitable for analytical workloads too.

Decision Intelligence

Continuously interpreting data, discovering patterns and making timely decisions based on historical and real-time data, the so-called Continuous Intelligence, will play a crucial role in defining the business strategies and will be one of the most widespread applications of machine learning.

Indeed, Gartner estimates that, within 3 years, more than 50% of all business initiatives will require continuous intelligence and, by 2023, more than one-third of enterprises will have analysts practicing decision intelligence, including decision modeling.

MLOps and AI Engineering

MLOps and AI Engineering focus on the operationalization of AI models to integrate them with the software development lifecycle and make them robust and reliable. According to Gartner analysts, it will generate three times more value than most enterprises not using it. This approach aims to make ML-powered software reproducible, testable, and evolvable, ensuring that models are deployed and updated in a controlled and efficient manner.

The importance of MLOps lies in the ability to improve the speed and reliability of ML model deployment, while reducing the risk of errors and improving the overall performance of models.

Data Democratization

Data lakes alone represent an operational cost rather than a source of business value. The true potential of data emerges when it’s transformed into actionable knowledge and insights. To realize this value, organizations may benefit from making data accessible to relevant stakeholders across the company. This approach involves developing appropriate data literacy competencies and establishing suitable access protocols.

When treated as a strategic resource, data can be utilized effectively throughout an organization. Leadership teams might consider reviewing existing processes to facilitate appropriate data access where needed, potentially addressing procedural complexities and access constraints.

Implementing balanced data accessibility practices can help organizations utilize their data resources more effectively. Providing team members with appropriate data access may support informed decision-making processes and contribute to operational improvements.