Generative AI Consulting Services
We implement Gen AI solutions that personalize sales, build user engagement, and save your time.
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We empower 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
Let’s talkWe 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:
Discovering 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.
Confirming project assumptions
We prepare a test implementation that demonstrates the capabilities of Gen AI and allows client expectations to be verified.
Building 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.
Implementing, integrating, and testing the solution
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.
Monitoring and developing
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
Let’s talkDiscover 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.
Your data holds great potential. Ask us how to make the most of it
Why should choose Alterdata?
We combine expert experience, extensive technical knowledge, and a flexible approach to collaboration to create data solutions that are truly tailored to your organization’s needs.
Comprehensive End-to-End Implementation
We manage the entire process: from consulting and technology selection, through data warehouse construction, to the development, maintenance, and optimization of solutions. This ensures that our clients receive consistent support at every stage of their data-related work, without having to coordinate multiple independent vendors.
Data Expert Team
We bring together the expertise of data engineers, analysts, data scientists, IT architects, and business consultants to address both technological and business needs. Our team helps translate an organization’s goals into concrete solutions that effectively support decision-making and business growth.
Technology Neutrality
We choose tools based on the goal, not the other way around. We work with popular cloud and analytics technologies, including Google Cloud, Azure, AWS, Snowflake, Databricks, Power BI, Tableau, and Looker. Thanks to our extensive knowledge of these tools, we recommend the solutions best suited to the client’s situation, rather than pushing a single technology.
Flexible Model of Collaboration
We offer support exactly when you need it, ranging from individual specialists to a Data Team as a Service model, without the need to build a full in-house team. This allows you to quickly expand your organization’s capabilities and leverage expert knowledge in a way that aligns with your current needs.
Business-Specific Solutions
We design services and architecture tailored to specific requirements, budgets, industries, company sizes, and business objectives. We treat each implementation as a unique case to ensure that the technology supports the processes, workflows, and priorities of the organization in question.
Secure Architecture
We create scalable, secure solutions designed to support organizational growth, handle increasing data volumes, and facilitate migration to modern cloud environments. We ensure access control, stability, and scalability so that the data platform can grow alongside your business.
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.
FAQ
How will I evaluate the effects of implementing Gen AI solutions?
The effects of Gen AI implementation can be assessed primarily through increased productivity – in many cases, Gen AI solutions replace manual work, which translates into significant savings in time and resources. In addition, the quality of work performed by AI will be monitored based on jointly defined KPIs, which will allow the effectiveness and accuracy of the system’s operations to be measured.
In which industries will Gen AI solutions prove effective?
Thanks to Generative AI, it is easy to automate processes previously performed by humans, including personalization of offers and product recommendations in e-commerce, transaction data analysis in finance, and generation of texts and images for advertising in marketing. Automated customer service (chatbots) can be used in any company that has direct contact with customers.
How can I know if I have the right infrastructure to implement Gen AI?
At the beginning of our cooperation, our experts assess existing systems and, if necessary, help you expand or optimize your data pipelines, storage, and digital asset integration. This ensures that the Gen AI solutions implemented in your organization will be scalable, efficient, and perfectly suited to your IT infrastructure.
How long will it take to implement Gen AI solutions?
The implementation time depends on the scale of the project and the complexity of the solution. Typically, the implementation process takes from several weeks to several months. We tailor the schedule to your needs.
What if we make changes to the store? Will it still work?
Our solutions are designed to adapt to changes in your store. Regular updates and support ensure continuous functionality and effectiveness of the system.
Will the new technologies be compatible with our technology?
Generative AI is compatible with most modern technologies. Our approach takes existing infrastructure into account to ensure easy integration and scalability.
Is Gen AI a solution only for large companies?
Gen AI is a solution for companies of all sizes. Its scalability allows the system to be tailored to your budget and needs, making it suitable for both small and large enterprises.