AI and Data Science Services
We help companies gain a competitive advantage through customer experience personalization and event predictions.
Let’s talkWe drive the success of leaders:
Predict events, personalize CX, and boost efficiency with Data Science and Gen AI
Reach the right audience with the right content and personalized offers. Prepare for the future by acting today with greater precision. Discover the predictive power of Machine Learning and the automation capabilities of Generative AI.
Machine Learning
We create Machine Learning models that learn through data analysis and their own processes to forecast trends, predict events and outcomes, segment users, and simulate the effects of various business scenarios.
See moreGenerative AI
We implement Gen AI solutions that automate tasks, accelerate business processes, and support the extraction of key information from documents, their classification, and integration with existing systems, helping to optimize operations and personalize offers.
See moreWe support companies at every stage of implementing AI and Data Science innovations
From understanding needs, through building a system that meets them, to scaling and automation.
We analyze needs and the initial action plan
- we get to know the client’s needs and challenges
- we identify possibilities for applying AI and Data Science
- we choose the technology to address the identified problems
We design the solution
- we develop a detailed project implementation strategy
- during workshops with the client, we select the tools to execute tasks
Building readiness
We prepare the data
- we optimize data for training the Machine Learning model
- we collect and process data for use in the Gen AI model
We develop models and solutions
- we build, train, and validate ML models to create systems that solve problems
- we integrate Gen AI models with data and external systems
- we conduct experiments and optimize solutions
- we prepare code and model documentation
We implement and integrate
- we transfer trained models to the production environment, whether cloud-based or on-premise
- we integrate models with existing systems and applications
- we optimize performance and configure monitoring of work results
We monitor and optimize
- we continuously monitor model performance
- we retrain models when necessary
- we optimize performance, providing support and maintenance
We help scale and develop the solution
- we extend the model’s application to new use cases and stimulate further innovation in the client’s organization
- we assess the impact of the implemented solution and its business value
Consumption – obtaining value from data
Discover our clients’ success stories
How data-driven advertising management helped an AMS agency maintain its leading position.
For the AMS team, we created a reliable and user-friendly ecosystem by integrating key data from external providers, including traffic measurements from mobile devices.
Thanks to the solutions offered by Alterdata, AMS was able to provide clients with access to key metrics, giving them greater control over campaigns and optimization of advertising spend.
Implementation of Business Intelligence and integration of distributed databases in PŚO
For Polish Open Fiber, we built an advanced Data Hub architecture based on an efficient and scalable Google Cloud ecosystem, utilizing business intelligence solutions to enhance operational efficiencies. We implemented Power BI as a Business Analytics tool and also trained its users.
This improved data availability and accelerated the creation of interactive reports and dashboards.
Discover the benefits of working with Alterdata
End-to-end execution
We provide comprehensive support, from understanding your needs to maintaining and expanding with new features. We also provide ongoing assistance at every stage of the solution lifecycle for your company.
Tailored services
We create Gen AI and ML models tailored to your needs and budget. We consider your industry specifics, company size, business goals, and other key factors to provide you with maximum benefits.
Team of professionals
Alterdata specialists have the knowledge and years of experience in implementations across various industries. For your project, we select those who best understand your requirements.
Data team as a service
We provide you with the support of a dedicated team of data engineering and analytics experts, available whenever you need it. This also includes help in expanding your architecture with new functionalities and training employees.
Broad tech-stack
We use modern and efficient technologies selected according to client needs, to effectively achieve business goals. This allows us to create solutions that perfectly address organizational needs and support their growth.
Data security
We work within your environment and do not extract any data from it, ensuring its security. You decide which information we can access during our work.
Base your success on our expertise
Tech stack: the foundation of our work
Discover the tools and technologies that power the solutions created by Alterdata.
Google Cloud Storage enables data storage in the cloud and provides high performance, offering flexible management of large datasets. It ensures easy data access and supports advanced analytics.
Azure Data Lake Storage is a service for storing and analyzing structured and unstructured data in the cloud, created by Microsoft. Data Lake Storage is scalable and supports various data formats.
Amazon S3 is a cloud service for securely storing data with virtually unlimited scalability. It is efficient, ensures consistency, and provides easy access to data.
Databricks is a cloud-based analytics platform that combines data engineering, data analysis, machine learning, and predictive models. It processes large datasets with high efficiency.
Microsoft Fabric is an integrated analytics environment that combines various tools such as Power BI, Data Factory, and Synapse. The platform supports the entire data lifecycle, including integration, processing, analysis, and visualization of results.
Google BigLake is a service that combines the features of both data warehouses and data lakes, making it easier to manage data in various formats and locations. It also allows processing large datasets without the need to move them between systems.
Google Cloud Dataflow is a data processing service based on Apache Beam. It supports distributed data processing in real-time and advanced analytics.
Azure Data Factory is a cloud-based data integration service that automates data flows and orchestrates processing tasks. It enables seamless integration of data from both cloud and on-premises sources for processing within a single environment.
Apache Kafka processes real-time data streams and supports the management of large volumes of data from various sources. It enables the analysis of events immediately after they occur.
Pub/Sub is used for messaging between applications, real-time data stream processing, analysis, and message queue creation. It integrates well with microservices and event-driven architectures (EDA).
Google Cloud Run supports containerized applications in a scalable and automated way, optimizing costs and resources. It allows flexible and efficient management of cloud applications, reducing the workload.
Azure Functions is another serverless solution that runs code in response to events, eliminating the need for server management. Its other advantages include the ability to automate processes and integrate various services.
AWS Lambda is an event-driven, serverless Function as a Service (FaaS) that enables automatic execution of code in response to events. It allows running applications without server infrastructure.
Azure App Service is a cloud platform used for running web and mobile applications. It offers automatic resource scaling and integration with DevOps tools (e.g., GitHub, Azure DevOps).
Snowflake is a platform that enables the storage, processing, and analysis of large datasets in the cloud. It is easily scalable, efficient, and ensures consistency as well as easy access to data.
Amazon Redshift is a cloud data warehouse that enables fast processing and analysis of large datasets. Redshift also offers the creation of complex analyses and real-time data reporting.
BigQuery is a scalable data analysis platform from Google Cloud. It enables fast processing of large datasets, analytics, and advanced reporting. It simplifies data access through integration with various data sources.
Azure Synapse Analytics is a platform that combines data warehousing, big data processing, and real-time analytics. It enables complex analyses on large volumes of data.
Data Build Tool simplifies data transformation and modeling directly in databases. It allows creating complex structures, automating processes, and managing data models in SQL.
Dataform is part of the Google Cloud Platform, automating data transformation in BigQuery using SQL query language. It supports serverless data stream orchestration and enables collaborative work with data.
Pandas is a data structure and analytical tool library in Python. It is useful for data manipulation and analysis. Pandas is used particularly in statistics and machine learning.
PySpark is an API for Apache Spark that allows processing large amounts of data in a distributed environment, in real-time. This tool is easy to use and versatile in its functionality.
Looker Studio is a tool used for exploring and advanced data visualization from various sources, in the form of clear reports, charts, and interactive dashboards. It facilitates data sharing and supports simultaneous collaboration among multiple users, without the need for coding.
Tableau, an application from Salesforce, is a versatile tool for data analysis and visualization, ideal for those seeking intuitive solutions. It is valued for its visualizations of spatial and geographical data, quick trend identification, and data analysis accuracy.
Power BI, Microsoft’s Business Intelligence platform, efficiently transforms large volumes of data into clear, interactive dashboards and accessible reports. It easily integrates with various data sources and monitors KPIs in real-time.
Looker is a cloud-based Business Intelligence and data analytics platform that enables data exploration, sharing, and visualization while supporting decision-making processes. Looker also leverages machine learning to automate processes and generate predictions.
Terraform is an open-source tool that allows for infrastructure management as code, as well as the automatic creation and updating of cloud resources. It supports efficient infrastructure control, minimizes the risk of errors, and ensures transparency and repeatability of processes.
GCP Workflows automates workflows in the cloud and simplifies the management of processes connecting Google Cloud services. This tool saves time by avoiding the duplication of tasks, improves work quality by eliminating errors, and enables efficient resource management.
Apache Airflow manages workflows, enabling scheduling, monitoring, and automation of ETL processes and other analytical tasks. It also provides access to the status of completed and ongoing tasks, as well as insights into their execution logs.
Rundeck is an open-source automation tool that enables scheduling, managing, and executing tasks on servers. It allows for quick response to events and supports the optimization of administrative tasks.
Python is a programming language, also used for machine learning, with libraries dedicated to machine learning (e.g., TensorFlow and scikit-learn). It is used for creating and testing machine learning models.
BigQuery ML allows the creation of machine learning models directly within Google’s data warehouse using only SQL. It provides a fast time-to-market, is cost-effective, and enables rapid iterative work.
R is a programming language primarily used for statistical calculations, data analysis, and visualization, but it also has modules for training and testing machine learning models. It enables rapid prototyping and deployment of machine learning.
Vertex AI is used for deploying, testing, and managing machine learning models. It also includes pre-built models prepared and trained by Google, such as Gemini. Vertex AI also supports custom models from TensorFlow, PyTorch, and other popular frameworks.
Bartosz Szymański
Data Strategy and Customer Relations Director