We’ll implement AI
in your organization
We design, train, and implement custom AI-based solutions (GenAI, LLM, Machine Learning).
Discuss your project
We empower leaders:
What AI solutions can we
implement in your company?
Generative AI & LLM
Integration and fine-tuning of models such as Gemini and GPT to meet an organization’s specific needs (e.g., internal advisory systems, automated content generation).
Conversational Analytics
Creating interfaces that allow users to ask business questions of data warehouses in natural language. This enables the instant generation of reports and charts without any knowledge of SQL.
RAG Systems
Building intelligent enterprise search engines and chatbots that enable employees to interact with thousands of documents, procedures, and knowledge bases in real time.
Document AI
Automation of document reading and processing. Using AI vision models to instantly extract structured data from invoices, contracts, scans, and handwritten documents.
Prediction and ML
Developing algorithms to forecast sales, demand, and customer behavior (churn), or to detect anomalies in operational processes.
Integration AI with Systems
Integrating AI algorithms into a company’s existing ecosystem: CRM, ERP, and e-commerce systems, as well as data warehouses.
Turn AI technology into business profits
Ask about implementationThe implementation process
from concept to production
We’ll guide your company through the entire process of safely adopting artificial intelligence. We don’t just leave you with the models—we take full responsibility for every stage of the project: from data analysis, through coding and rigorous security procedures, to post-implementation monitoring.
Product and Technology Workshop (Discovery)
We analyze your business processes and data architecture. Together, we define your goals, identify the most cost-effective areas for automation, and assess which ones will deliver the highest return on investment (ROI).
Data Readiness
AI is only as good as the data it works with. In this step, our data engineers clean, organize, and integrate the necessary data sources in a secure environment, laying a solid foundation for AI models.
Proof of Concept (PoC)
Within 2–4 weeks, we develop a simplified, functional version of the system using a sample of your real data. This allows you to safely test the system’s design and the effectiveness of its algorithms before committing your full implementation budget.
Model Selection and Training (Development)
We select the optimal technology (e.g., models from the Gemini family or open-source solutions). We design advanced prompt engineering or fine-tune models to accommodate the specific jargon of your industry.
Integration with the ecosystem (Deployment)
We deploy a ready-made solution within your cloud infrastructure (GCP / AWS / Azure). We integrate the AI system with the tools your team uses every day, such as CRM, ERP, Slack, Teams, and internal knowledge bases.
Governance and Security (AI Governance)
We implement strict access policies, query auditability, and data control mechanisms. We ensure that the AI system operates in accordance with the company’s security procedures and legal regulations, and that users have access only to the resources authorized for them.
Maintenance, monitoring, and development (MLOps)
After the system goes live, we continuously monitor its stability, response times, and cloud costs. We ensure that the system adapts to new data and scales to meet the needs of your business.
Ready to take the first step?
Schedule a workshopYou choose the experts
We serve as a bridge between Google’s raw computing power and real-world business processes. As a Google Cloud Partner, we don’t just provide technology, we design it to drive your bottom line.
▪️ Data experts: First, we organize your architecture so that AI operates on clean and reliable data.
▪️ Validation loop: We implement oversight mechanisms that allow your experts to easily verify the AI’s work, eliminating errors.
▪️ Cost transparency: We optimize the use of models on the Vertex AI platform so that you only pay for the work actually performed by the Agents.
Discover our clients’ success stories
We helped Celsium build a data warehouse that reduced costs by PLN 180,000 per year
We integrated data from meters, SCADA, billing, and weather systems into a single data warehouse on Google Cloud Platform. We created advanced ETL processes, data quality control mechanisms, and dashboards in Tableau to support daily analysis of heat production and consumption.
The result? Meter failures detected in one day (previously one month), operational data updated three times a day, and significant savings thanks to heat source optimization and better demand balancing.
We built a modern data warehouse in GCP for PŚO
We helped Polski Światłowód Otwarty design and implement a scalable Data Lake architecture on Google Cloud Platform. We integrated 13 data sources, created automated ELT processes, access security, and a data model that serves as a single source of truth within the organization.
The result? Independence in reporting, rapid integration of new systems, readiness for future needs, and cost savings by eliminating on-premise infrastructure.
We helped AMS leverage data from DOOH media and maintain its position as a leader in outdoor advertising
We built a modern data ecosystem for AMS, a leader in OOH and DOOH advertising. We combined data from media, internal systems, Proxi.cloud, and CitiesAI to create a unified data warehouse in BigQuery with near real-time analysis.
The result? Data-driven targeting, campaign automation, better results for customers, and a stronger market position thanks to programmatic buying based on actual reach.
We helped Tutlo automate data integration and build a modern real-time ETL
In collaboration with the Tutlo team, we designed and implemented a data integration architecture based on serverless Google Cloud components. The system enables data synchronization from dozens of sources—including CRM—with full monitoring, CI/CD automation, and readiness for further scalability.
The result? A stable and flexible data ecosystem, ready for process automation, ML projects, and dynamic development of the educational platform.
We helped FunCraft forecast ROI and optimize UA budgets in the mobile gaming industry
We implemented a comprehensive BI solution for an American game studio, integrating data from Adjust, stores, and advertising platforms into the BigQuery warehouse. We built advanced dashboards in Looker Studio and predictive ROI models that enable accurate budget decisions—even with a long return on investment cycle.
The result? The FunCraft marketing team works faster, more efficiently, and with full control over their data.
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 do you begin implementing AI in your company, and where should you start?
It’s a good idea to start the process by auditing business processes and identifying areas with the highest potential return on investment (ROI). The most common first step is the Discovery phase (technology workshops), during which the quality of existing data is analyzed and a single, smaller task is selected to be implemented as a Proof of Concept (PoC).
How much does it cost to implement artificial intelligence (AI) in a company?
The cost of implementing AI depends on the complexity of the project, the volume of data, and the technology chosen. The initial testing phase (PoC prototype) typically costs between several thousand and several tens of thousands of zlotys. Full implementations of Enterprise systems are priced on a case-by-case basis, and operating costs (OpEx) are optimized through a pay-per-use model for cloud resources.
What are the biggest risks associated with implementing AI in business?
The main risks include: poor-quality input data (leading to erroneous AI conclusions), lack of control over the costs of model queries (cloud costs), and team resistance to adopting the new tool. All of these issues can be addressed through proper system architecture design, MLOps monitoring, and employee training.
Is deploying LLM models (such as Gemini) safe for company data?
Yes, provided the environment is properly configured. Commercial deployments are carried out within the customer’s closed and secure cloud infrastructure. This ensures that confidential information, databases, and trade secrets never leave the organization and are not used to train public AI models.
How long does it take to implement a custom AI solution?
Developing a working prototype (PoC) and validating business assumptions typically takes 2 to 4 weeks. Full production implementation – including integration with CRM/ERP systems, building data pipelines, and security testing—usually takes 2 to 5 months, depending on the scale of the project.
How does an off-the-shelf AI tool differ from a custom implementation?
Off-the-shelf tools (SaaS) offer a quick start, but they don’t understand the specifics of your business, have limited integration capabilities, and can result in high per-user licensing costs. A dedicated AI implementation is tailored to your company’s unique processes, learns from your data, and becomes your company’s intellectual property.
What business systems can be integrated with artificial intelligence?
AI models can be integrated with virtually any modern software that has an API. Most commonly, integration is performed with CRM systems (e.g., Salesforce, HubSpot), ERP systems (e.g., SAP), e-commerce platforms, knowledge bases, and modern cloud-based data warehouses (BigQuery, Snowflake).
What technical capabilities must a company have before implementing AI?
The organization does not need to have its own team of AI engineers or data scientists – the implementation partner handles all technical aspects, from architecture to coding. However, it is essential for the company to have a “business owner” (Product Owner) who understands the operational processes and the goals that AI is intended to achieve.
In which business areas does AI yield the greatest cost savings?
The fastest return on investment is seen in the automation of repetitive processes: intelligent document processing (Document AI), customer service automation (multilingual bots), advanced predictive analytics (sales and demand forecasting), and marketing automation.
What is the cost of maintaining such a solution (cloud costs and AI licenses)?
Effectiveness is measured using hard KPIs established before the project begins. These typically include: shortening the duration of a given process (e.g., reducing invoice processing time from hours to seconds), reducing the number of human errors, cutting operating costs, or directly increasing conversions and sales through accurate AI recommendations.