AI Solutions and Automation for European Businesses
The AI hype has settled, and the technology has matured. It's no longer just about generating text and images. It's about deploying AI agents and intelligent systems in production that solve real, mundane, and important tasks – driving digital transformation across your organisation.
To succeed with this in a European business context, an OpenAI license isn't enough. It requires proper software development, control over your data, and regulatory compliance with GDPR and the EU AI Act.
Our approach is grounded. We focus on the architecture – the glue that connects the new, smart AI models with the core systems you already use. That's how we create solutions that actually work in everyday operations, delivering stable value over time.
What Do You Actually Need? (Three Levels of AI)
These are the "training wheels." Tools that help employees write emails, code, or analyze data faster. Humans control the process, but AI provides more power.
Systems that make your company's internal data (PDFs, SharePoint, databases) accessible through natural language. It's about finding the right information quickly and accurately, without digging through folder structures.
This is where we go from talking to doing. An agent can read an email, look up information in an ERP system, perform a calculation, and prepare an action for approval.
RPA vs. AI: What's the Difference?
RPA (Robotic Process Automation) has long been the standard for automating repetitive tasks. But it's important to understand the difference: RPA follows rigid rules and clicks through screens – it's "dumb" automation that breaks when the interface changes.
Artificial intelligence and machine learning add what RPA lacks: understanding and adaptability. Where an RPA bot stops when an invoice format changes, an AI solution can interpret the content and handle variations. We build the intelligent layer on top – systems that actually understand what they're working with.
For organisations with existing RPA investments, we can integrate machine learning to make your automation more robust and intelligent. It's not about replacing everything, but about elevating your solutions to the next level.
No Magic Without Good Data
A language model is only as good as the data it's fed. The most common obstacle to successful AI adoption is attempting to connect advanced models to unstructured data. This results in inaccurate answers and potential security vulnerabilities.
We build solutions based on RAG architecture (Retrieval-Augmented Generation). Simply put, this forces the AI to base its answers on facts you actually have. This significantly reduces the risk of "hallucinations."
How We Ensure Precision
- We establish pipelines that fetch data from sources like SharePoint or SQL, cleaning them of noise before indexing.
- Data is converted to "vectors" so the system understands meaning, not just keywords. This provides accurate searches even with complex queries.
- Security is critical. We mirror your company's access rights. An employee should never receive AI-generated answers based on documents they're not authorized to read (e.g., executive salaries or HR minutes).
Technology We Use for AI Solutions
- OpenAI, Claude, Gemini, DeepSeek – we choose the right model for the task
- Self-hosted models via Ollama when data is sensitive
- Microsoft Copilot Studio for simpler use cases
- Qdrant for semantic search and RAG
- Meilisearch for full-text search
- PostgreSQL with pgvector for simpler setups
- Python with FastAPI for ML services
- Laravel for API and business logic
- Celery and Redis for asynchronous jobs
When AI Actually Does the Work (Agentic AI)
The transition from a passive chatbot to an active agent is about connecting the technology to your business systems, whether it's Salesforce, Visma, or SAP. This is where we move from talking about things to getting things done.
Examples of Tasks AI Agents Can Solve
Imagine an agent that monitors public procurement portals, downloads tender documentation, compares requirements against your company's CV database, and creates a first draft response. This saves enormous amounts of manual work.
An agent can handle administrative tasks for new hires – ordering equipment, creating user accounts in systems, and answering questions, freeing HR to focus on the human interactions.
Instead of manual checks, an agent reads incoming invoices and matches line items against orders in the ERP system. It approves what matches and alerts a human only when it finds discrepancies.
Enterprise Chatbots and AI Assistants
Many businesses start their AI journey with a chatbot. It can be a good starting point – but a chatbot is only as good as the systems it's connected to. A standalone chatbot that only answers general questions provides limited value.
We build chatbots and AI assistants that are integrated with your business systems and data. This means the chatbot can actually help customers with order status, employees with HR questions, or sales teams with product information – based on real-time data from your core systems.
What Separates a Good Chatbot from a Poor One?
- Connected to CRM, ERP, helpdesk and other business systems via secure APIs.
- Understands who the user is and adapts responses accordingly – a customer receives different information than an employee.
- Knows when to hand over to a human, and transfers the entire conversation history seamlessly.
EU AI Act, GDPR, and Responsible AI
The EU AI Act sets new requirements for how artificial intelligence can be used in business. Although the regulation is not yet formally implemented in all jurisdictions, it's right around the corner. The requirements will affect European businesses, especially AI systems in HR, recruitment, and critical infrastructure.
We take no chances on compliance. We build GDPR-ready AI solutions that meet future EU AI Act requirements today:
Traceability: You should be able to see exactly which source documents the AI based its answer on.
Logging: All interactions are logged for auditing and quality assurance.
Human in the Loop: Critical decisions are designed so they always require human approval before execution.
We also ensure that solutions meet accessibility requirements (WCAG), making them accessible to all employees.
The Path from Sandbox to Production
We prevent projects from stalling after the test phase by planning for operations from day one.
Technical Assessment: We evaluate data quality and infrastructure. Is the data in the cloud? We identify use cases with low risk and high value.
Prototyping (MVP): We build a working solution in a closed environment to validate that the technology actually solves the problem.
Security Testing: We stress-test the solution (Red Teaming) to uncover vulnerabilities or potential data leaks.
Deployment: Rollout with monitoring, training, and a clear model for maintenance.