Generative AI Services & Consulting
We implement Gen AI solutions that personalize sales, build user engagement, and save your time.
Let’s talkWe drive the success of leaders:
Gen AI increases customer satisfaction and company efficiency
We help you leverage the potential of data through Generative AI.
Automatic document analysis
Scripts and efficient analysis of thousands of reports, invoices, or product documents free up time for more creative and strategic actions, without the risk of human error in data interpretation.
Always available customer service
Chatbots and virtual assistants are ready to answer user questions 24/7. Thanks to automated support, you don’t need to maintain an expensive, expanded customer service department.
High-quality content generation
Gen AI supports the creation of complex marketing content, such as social media posts and articles, and thanks to integration with organizational data, it has a complete vision of the company and its products.
Search by description and image
Buyers describe the desired item with words or attach a picture of a similar one. AI suggests the best-matching products from your assortment – innovations that provide a competitive edge.
Intelligent data processing
Gen AI automatically organizes, tags, and summarizes documents, photos, and videos, making it easier to search and manage knowledge within your organization.
Increased offer personalization
Gen AI analyzes customer behavior and, based on it, recommends offers in email and notifications. This ensures that the products are precisely tailored to their needs. As a result, effectiveness increases, and the customer associates your brand positively.
Discover more Gen AI applications
We create comprehensive solutions based on Gen AI
Our services solve real business problems, ensuring a fast return on investment.
Knowledge and experience at every stage of the process:
Discover business and technological needs together
We get to know your problems and business requirements as well as the potential of company data from the perspective of its usefulness for Gen AI. We select the technology that works best for this particular application.
We confirm project assumptions
We prepare a test implementation that demonstrates the capabilities of Gen AI and allows client expectations to be verified.
We build the production version
After accepting the solutions presented at the proof of concept stage, we use the gathered insights and feedback to perfect the final version of the Gen AI solution.
We implement, integrate, and test
We implement the final solution in the production environment and integrate it with your systems. We conduct final tests to ensure the solution works as expected and integrates smoothly with your company’s infrastructure.
We monitor and develop
We monitor the implemented solutions to ensure maximum performance and compliance with your requirements. If necessary, we expand the solution by adding new functionalities.
Accelerate your daily work with Gen AI
Discover the benefits of working with Alterdata
End-to-end execution
We provide comprehensive support and continuous assistance at every stage of the Gen AI solution lifecycle. After implementation, we support maintenance, development, and expansion with new features.
Szeroki tech stack
Stosujemy nowoczesne i wydajne technologie, dobierając je tak, by najefektywniej realizowały cele. Pozwala to nam budować modele idealnie dopasowane do potrzeb.
Team of professionals
Our data engineers and analysts have the knowledge and experience to implement Gen AI across various industries. We select specialists for projects who understand your requirements.
Tailored services
We create Gen AI solutions 100% aligned with your business goals and ready to solve your problems. We consider the industry, company size, your goals, and other important factors.
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.
Data team as a service
You receive support from a dedicated team of experts, available whenever you need it. This also includes assistance in expanding your Gen AI and training your team to use it.
Reduce operational costs with Gen AI
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.
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