AI Development - Local Hardware | EARNST

What we build

AI systems that run on your own hardware -- dedicated GPU servers in your data center, not someone else's cloud. This includes document analysis (extracting information from PDFs, contracts, invoices), RAG systems (question answering based on your internal documents), text classification (routing, categorization, sentiment analysis), and custom workflows (automating repetitive tasks that require language understanding).

We work with open-source LLMs (Llama, Mistral, Phi) running on local hardware, vector databases for semantic search, and custom fine-tuning when necessary. The key difference from typical AI products: your data never leaves your building. No OpenAI API calls. No cloud dependency. No surprise pricing changes. Just models running on hardware you own, processing data you control, with performance you can predict.

Who needs this?

Businesses with data that can't be sent to external APIs for legal, competitive, or compliance reasons. Common scenarios: law firms analyzing contracts, healthcare organizations processing patient data, financial services handling sensitive documents, manufacturers with proprietary product information, HR departments processing applications and employee data.

If you're already using ChatGPT or similar services but feel uncomfortable about data privacy, or if you've been told "we'd use AI but GDPR won't allow it," this is the solution. Also relevant for companies processing high volumes where API costs would exceed the cost of running your own infrastructure.

How EARNST approaches it

We start by questioning whether AI is the right solution at all. Many problems can be solved with simpler, more reliable methods (rules, search, traditional ML). If AI genuinely helps, we prototype with the simplest model that could work, test it on real data, and measure whether accuracy meets business requirements. Only then do we move to production infrastructure.

Infrastructure runs on your own local servers -- dedicated GPU machines (NVIDIA A100, H100, or RTX 4090/L40S for smaller models). We use Docker for deployment, implement proper monitoring (response times, accuracy, resource usage), and set up fallback mechanisms for when models fail. We document limitations clearly. AI models make mistakes, we design systems that account for this reality rather than pretending everything works perfectly.

Project scope

A feasibility analysis (can AI solve this problem?) takes 1 to 2 weeks and includes prototype testing on sample data. Full implementation timelines vary significantly: a document extraction system (parsing invoices, extracting key fields) takes 4 to 6 weeks. A RAG system (internal knowledge base with question answering) takes 6 to 8 weeks. Complex custom workflows (multi-step automation with human oversight) can take 10 to 14 weeks.

Infrastructure costs depend on volume and model size. Smaller models run on standard servers (€100 to €300/month). Larger models or high volume require GPU instances (€500 to €2,000/month). We optimize for cost efficiency: using the smallest model that achieves required accuracy, batching requests, caching results.