AI and Data Science Consulting Services
Our AI and Data Science services help companies gain a competitive advantage through customer experience personalization, and event predictions.
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We empower leaders:
We 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 your needs and develop a preliminary action plan
We learn about the client’s needs and challenges, identify opportunities for applying AI and data science, and then select the technologies that best suit the defined problems
We design solutions
We develop a detailed project implementation strategy, and during workshops with the client, we select the appropriate tools and technologies to best achieve the set goals.
We prepare data
We optimize data for training Machine Learning models and collect and process it so that it can be effectively used in Gen AI models.
We develop models and solutions
We build, train, and validate ML models to create systems that effectively solve specific problems. We integrate GenAI models with external data and systems, conduct experiments, and optimize solutions. We supplement the whole process with complete code and model documentation.
We implement and integrate
We transfer trained models to a production environment in the cloud or on-premises. We integrate them with existing systems and applications, optimizing performance and configuring monitoring of their operation.
We monitor and optimize
We continuously monitor model performance. If necessary, we retrain them, ensuring optimization, ongoing support, and maintenance.
We help scale and develop solutions
We extend the models to new use cases, supporting further innovation within the organization. At the same time, we assess the impact of the implemented solution and its business value.
See how we can help your data
Let’s talkPredict events, personalize CX, and boost efficiency with Data Science and Gen AI
Reach the right audience with the right content and personalized offers. Data science consulting provides actionable insights that enhance decision-making. Prepare for the future by acting today with greater precision.
Discover the predictive power of Machine Learning and the automation capabilities of artificial intelligence and 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.
To achieve this, we employ optimal data science techniques in developing these models, ensuring they are effective and aligned with business goals.
Generative AI
We implement Gen AI solutions with natural language processing to automate tasks, accelerate business processes, and extract, classify, and integrate key information from documents, optimizing operations and personalizing offers.
See moreDiscover our clients’ success stories
We automated document processing for Nexery and recovered over 2,000 hours of team work time
We helped Nexera manage its growing collection of documents related to leased infrastructure. Using GenAI technology and our proprietary data extractor, we automated the processing of nearly 30,000 documents—decisions, contracts, attachments—and created a document classifier and tools for exporting data to the company’s internal systems.
The result? Savings of over 2,000 working hours, full control over liabilities, elimination of payment errors, and realistic cost forecasts. The Nexera team gained new skills and a ready-made base for further automation of document processing.
We increased Tutlo user engagement with machine learning models
We helped the Tutlo educational platform better understand student and teacher behavior by implementing a personalized ML model in the BigQueryML environment. By analyzing over 80 variables and segmenting users, we created a precise model that predicts student engagement, which translated into a more personalized learning experience.
The result? 80% prediction accuracy, faster business decisions, greater motivation to learn, and more intuitive use of the platform—all without the need to migrate data between systems.
We reduced storage costs by 30% for an e-commerce company
We helped an e-commerce client organize data from multiple sales channels, build a data warehouse in BigQuery, and implement ML models to forecast demand and optimize inventory levels. We automated ordering processes by integrating algorithms with logistics operations.
The result? 30% less excess inventory, 15% higher sales of bestsellers, and significant savings thanks to better purchasing planning and reduced sales.
We helped AMS transform data from advertising media into measurable campaign results
In cooperation with the leader in OOH/DOOH advertising, we have created a scalable data warehouse and integrated external sources such as Proxi.cloud and CitiesAI. Thanks to the implementation of BigQuery and Machine Learning, AMS can now plan advertising campaigns in near real time, target them based on audience behavior, and analyze their effectiveness with unprecedented precision.
The result? Higher return on investment for customers, better campaign targeting, and maintaining a leading position in the era of advertising digitization.
Discover the benefits of working with Alterdata
End-to-end execution
Our experts provide comprehensive support, from understanding your needs to building and maintaining data science solutions, leveraging our data scientists’ expertise in data engineering and data analytics.
We also offer ongoing assistance at every stage of the solution lifecycle to ensure continuous growth and optimization for your company.
Tailored services
We create Gen AI and ML models tailored to your needs and budget, leveraging our expertise in data science projects.
Our experts consider your industry specifics, company size, business goals, and other key factors to deliver 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 science consultants 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
Your data holds potential.
Ask us how to unlock it
FAQ
How will I be able to evaluate the benefits of implementing AI and Data Science solutions?
At the start of our collaboration, we will define and monitor the most important KPIs for your company, such as forecast accuracy, cost reduction related to content generation or customer service, and improved analysis efficiency.
Are AI and Data Science solutions tailored to the company’s needs and scalable?
Yes, our AI and Data Science solutions are fully tailored to the individual needs of your company. We design them with scalability in mind, based on anticipated requirements defined at the start of the collaboration. This ensures that the systems will grow alongside your company, adapting to changing needs and increasing business scale.
Does an external analyst or data engineer have access to all the information in our company?
We ensure complete data security. Access to information is strictly controlled, and our experts only have access to the data necessary for the project, adhering to the highest protection standards. We do not extract data; it is stored exclusively on the client’s side.
Is the company technologically objective and will it consider our technology preferences?
Alterdata is technologically independent. Our recommendations are always based on your preferences and the best solutions available on the market, ensuring optimal effectiveness and compliance with your requirements. We partner with many technology providers but do not sell their products. This gives us maximum objectivity in selecting the most suitable technology to solve your problem.