Artificial Intelligence Solutions

The cost of developing an artificial intelligence solution depends on the project type and complexity. A simpler AI chatbot or automation project typically starts from EUR 3,000-10,000, while a complex custom AI system (RAG knowledge base, predictive model, AI agents) can range from EUR 10,000-50,000+. Pricing factors include data volume, number of integrations, required infrastructure, and model fine-tuning needs. Every AI project starts with a free consultation and feasibility analysis so you know exactly what to expect.

The type of data needed depends on the AI system. For a RAG-based knowledge base system, your existing company documents (PDFs, Word files, emails, internal wikis) are sufficient. For chatbot development, previous customer service communications and FAQ databases are useful. For predictive analytics, historical business data (sales, customer behavior, financial data) is required. Most AI projects can start from existing, unstructured data -- data cleaning and preparation is our responsibility.

Data security is the most important consideration in AI development, and we take it extremely seriously. With local LLM deployment, we ensure your sensitive data never leaves your servers -- the model runs on your infrastructure with full data sovereignty. If you choose cloud-based solutions, we implement encrypted connections, anonymization techniques, and access controls. Every AI solution is developed GDPR-compliant, including data minimization, legal basis management, and full auditability.

RAG (Retrieval Augmented Generation) is an AI architecture that connects large language models (LLMs) with your company's own knowledge base. Instead of relying on the AI's general knowledge, the RAG system first searches for relevant documents in your database, then generates answers based on those sources. As a result, answers are accurate, up-to-date, and backed by source citations. It's ideal for internal knowledge bases, customer service systems, legal document analysis, and product knowledge systems.

AI doesn't replace employees -- it amplifies them. The AI solutions we develop automate repetitive, time-consuming tasks (data processing, document analysis, report generation), so your team can focus on value-creating, creative work. In our experience, AI adoption typically reduces time spent on routine tasks by 30-60%, while employee satisfaction increases because they can work on more interesting assignments instead of monotonous tasks. AI is your team's most powerful tool, not its replacement.

AI investment ROI depends on the project type and company size, but typically pays off within 3-12 months. An AI-powered customer service chatbot can reduce support costs by 40-60% in the first month alone. For process automation, savings are measured in human work hours -- if a workflow previously took 20 hours per week and AI automates 80%, the payoff is nearly immediate. Predictive analytics increases revenue through better decision-making. We calculate expected ROI as part of every proposal.

AI development (artificial intelligence development) is building software and systems that use machine learning, large language models (LLMs), or other AI algorithms to perform tasks. A typical AI project has 4 phases: (1) use case definition, (2) data preparation and cleaning, (3) model development or LLM integration, (4) production deployment with continuous fine-tuning. Typical AI projects: chatbot development, RAG knowledge base, document classification, predictive models, AI agents, image recognition.

AI integration: building ready-made AI tools (OpenAI API, Claude API, Gemini) into your existing systems -- fast, cheap, 1-3 months. AI development: training a custom model on your data (fine-tuning), running your own LLM on-prem, or building an entirely new AI architecture -- 3-12 months, higher cost, but full data sovereignty and customisation. SMBs typically need AI integration -- business application of existing AI models. Larger companies or those handling sensitive data (finance, healthcare, legal) often benefit from custom AI development.

5 steps: (1) AI AUDIT: pick 3-5 recurring document-heavy or decision-heavy processes where AI could help. (2) PILOT: launch a small project (e.g. RAG chatbot on internal documents), with measurable results in 4-8 weeks. (3) MEASURE: quantitative ROI -- time saved, errors reduced. (4) SCALE: build on the successful pilot. (5) AI CULTURE: training, AI champions, ethical guidelines. Never start with a big AI platform decision -- prove the concept on a small project first.

Modern LLMs (GPT-4, Claude, Gemini) work excellently in most major languages -- often the same quality as English. RAG systems work just as accurately on multi-language documents. For custom fine-tuned models in smaller languages, training data preparation takes a bit more effort (smaller corpora online), but in our experience 90%+ accuracy models can be built for language-specific tasks. For specialised domains (e.g. local legal, medical) language-specific fine-tuning is recommended -- we work with domain partners on these.