LLM Integration: The Fastest Entry Point
With modern Large Language Models (LLMs) like GPT-4, Claude, or Gemini, you can bring AI features to your software within just a few weeks. Whether text generation, summarization, translation, or customer service – the APIs are powerful and well-documented.
The effort mainly lies in prompt engineering (asking the right question) and integration into your existing software. For many use cases, this is the most affordable path to AI.
RAG: Your Data + LLM = Real Knowledge
Retrieval Augmented Generation (RAG) combines LLMs with your own knowledge base. Instead of only answering based on general training, the AI accesses your documents, FAQs, and internal information.
This drastically reduces hallucinations (incorrect answers) and delivers reliable, verifiable information. Ideal for customer service, internal knowledge management, and sales support.
When to Choose Custom ML?
Training custom machine learning models is worthwhile for:
- Specialized data: Images, sensor data, industry-specific texts
- Data privacy: No data sent to external APIs
- Domain expertise: Train models on your specialized terminology
- Edge deployment: Run models locally/offline
Plan for Ongoing Costs
AI projects have significant ongoing costs:
- API costs: GPT-4 costs approx. $0.01–0.03/1K tokens – with high volume quickly €500–2,000/month excl. VAT
- Infrastructure: Vector databases, GPU compute, hosting: €200–2,000/month excl. VAT
- Maintenance: Prompt optimization, model updates, monitoring: €500–2,000/month excl. VAT
