Document AI
We implement DocAI systems based on LLM models that understand document context just like humans do, while operating at processor speed.
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Why is your current data processing
holding your business back?
Most companies have vast amounts of knowledge locked away in unstructured documents that traditional OCR systems cannot interpret correctly. Relying on manual verification and outdated tools creates bottlenecks that prevent the effective scaling of operations and the implementation of advanced AI-based analytics.
Hidden operating costs
Your team spends thousands of hours a year on tedious, manual analysis of invoices and contracts, which drastically increases back-office costs.
The risk of making the wrong decisions
Manual data entry inevitably leads to errors that compromise data quality and result in actual financial losses and compliance issues.
Lack of flexibility and scale
Traditional solutions fall short when dealing with non-standard layouts and low-quality scans, making it impossible to automate document processing in real time.
Knowledge trapped in silos
Key business information remains inaccessible to business intelligence systems because it has not been converted into a machine-readable format.
Turn unstructured data into a smart knowledge base
Contact usEkosystem Document AI,
Intelligent Data Analysis
Our approach to Document AI goes beyond simple OCR. We build systems based on LLM models that understand the structure and logic of your documents. We transform unstructured data into dynamic knowledge bases, ready for immediate use in business processes.
Semantic Understanding
The system accurately interprets context, distinguishing key data (e.g., amounts, dates, terms) regardless of the document template.
Automatic classification
AI algorithms automatically recognize the file type (addendum, invoice, application) and instantly route it to the appropriate process.
RAG Architecture
We create vector databases that enable interactive engagement with your archive and instant access to distributed knowledge.
Business-Ready Data
We provide structured data that feeds directly into your data warehouse and analytics systems.
Smart GenAI solutions tailored to your needs
Choose a ready-made GenAI automation workflow and solve your organization’s key challenges. We combine the power of LLM models with your data to deliver applications that tangibly improve operational efficiency.
AI in HR recruitment processes
Smart CV scoring and candidate match analysis. Automatically identify skill gaps and optimize your recruitment processes.
Check the solutionAI for analyzing customer feedback
Instant sentiment analysis of thousands of customer reviews. Turn scattered feedback into a ready-to-use product strategy and build brand loyalty.
Check the solutionAI for analyzing thousands of documents
A precise understanding of the context of invoices and contracts. Eliminate manual errors and ensure full compliance in real time.
Check the solutionSchedule a free consultation
Contact usOur experience with Document AI
We helped Nexer optimize its data processing for 30,000 documents
For a leader in fiber-optic infrastructure, we implemented the Document AI system, which reduced the time required to analyze technical documentation by over 2,000 hours. Using Gemini models, we automated data extraction from lease agreements and site selection decisions, eliminating manual errors.
Result? Identified savings potential exceeding
1,000,000 PLN annually, along with full real-time control over deadlines and costs of obligations.
Data that drive business: GenAI in document structuring (1-2.04.2025)
Sławomir Mytych (Alterdata) demonstrates how GenAI models are revolutionizing document management—from automatic classification to precise data extraction. See a live demo of the application that transforms the chaos in your files (invoices, policies, resumes) into structured business insights.
In this recording, you’ll learn:
Process automation: How to configure LLM models to extract data in a format perfectly suited to your systems.
The potential of Gemini: How AI’s contextual understanding of content supports daily operations and eliminates manual errors.
Implementation economics: How much does document processing really cost, and why it’s worth starting with a quick PoC.
Not sure where to start?
Download our e-book
What will you find in the e-book?
In this guide , you’ll learn how to conduct a documentation audit to identify the processes with the greatest potential for automation. You’ll discover a proven roadmap for GenAI implementation, from building an MVP to safely scaling the solution across the entire company. You’ll also understand how to manage the validation loop and your team to eliminate model errors and ensure the highest quality of business data. The guide concludes with practical checklists that will prepare your organization for a real digital transformation.
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
What is Document AI (DocAI), and how does it differ from traditional OCR?
Traditional OCR simply reads characters, whereas Document AI (which uses LLM models) understands semantic context, enabling intelligent data extraction from any PDF or scanned document, regardless of its layout.
What types of documents can be processed using Document AI?
Technology goes far beyond standard administrative tasks. We have unique expertise in processing highly specialized data, such as complex district heating network maps and industry-specific documentation for telecommunications installations. In addition to handling technical documentation, the system fully automates the workflow of business documents: from cost invoices and commercial contracts to complaint forms and advanced CV screening in HR departments. Thanks to LLM models, Document AI can interpret context even in the most non-standard industry formats.
What is RAG (Retrieval-Augmented Generation) in the context of working with documents?
The RAG architecture enables the integration of large language models (LLMs) with vector databases that store the knowledge embedded in your documents. This allows you to create a secure assistant that your employees can “talk to” within the company.
Does Document AI integrate with ERP and CRM systems?
DocAI solutions are designed as modules that integrate easily with standard systems such as SAP, Microsoft Dynamics, and Salesforce, ensuring a seamless flow of structured data into operational processes.
How much does it cost to implement Document AI, and what factors influence the cost?
Implementation costs depend on the scale (number of pages processed), the complexity of the documents, and the level of integration. We offer flexible models that allow for cost optimization, and our partners typically see a return on investment (ROI) in less than six months.
How does Document AI help optimize HR processes?
The GenAI for HR solution automates, among other things, candidate screening by analyzing hundreds of resumes and scoring the match between soft and hard skills. This drastically reduces recruitment time and eliminates manual errors.
Are my documents secure when using LLM models? How do you ensure compliance?
Processing takes place in your private cloud, with no data leakage to public models. Our architecture ensures full compliance with regulations and the highest data protection standards, including RAG, while maintaining access controls.
How does AI handle hallucinations and errors in documents?
We use advanced verification systems and data validation loops. Each structured result is compared with the original file, which minimizes the risk of errors (hallucinations) and ensures the highest quality of business data.
What does the implementation process (roadmap) for Document AI look like at Alterdata?
The process begins with an audit and the development of an MVP/PoC (Proof of Concept) within 4–6 weeks, which allows for rapid validation of the assumptions. We then carry out a full implementation and scale the system across the entire organization, integrating it with a modern data warehouse.
Can Document AI process documents in multiple languages, including low-quality scans?
Yes, our models are multilingual and capable of contextual analysis. We use advanced image processing techniques to accurately interpret data even from noisy, low-quality scans, eliminating errors associated with manual data entry.