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AI Use Cases

Welcome to the AI Use Cases section. Here you will find practical guides, best practices (design patterns), and code examples to help you efficiently integrate our Large Language Models as a Service (LLMaaS) into your business processes and applications.

Our platform runs entirely on high-performance GPU infrastructure in Switzerland. This ensures that your data and prompts are protected at all times, meeting the highest compliance and data protection standards.

Overview of Typical Use Cases

Here are the most frequently implemented scenarios on our platform:

1. Retrieval-Augmented Generation (RAG)

Combine your internal knowledge bases (e.g., documents, wikis, or databases) with LLMs to generate precise, context-aware answers. * Relevant Endpoints: /v1/embeddings for vectorization, /v1/rerank for optimized search ranking, and /v1/chat/completions for generating responses. * Recommended Models: ew/qwen3-embedding-4b and ew/qwen3-reranker-4b for search, and ew/minimax27 or ew/deepseek32 for text generation.

2. Classification and Data Extraction

Structure unstructured data (e.g., emails, support tickets, or reports) automatically. You can extract metadata, analyze sentiment, or automatically route tickets to the correct department. * Advantage: Fast processing times and high accuracy through the use of structured output (JSON mode) or tool calling. * Recommended Models: ew/gemma4 or ew/qwen3.6-35B-A3B for fast and cost-effective classifications.

3. AI Agents and Workflow Automation

Build autonomous or semi-autonomous agents that can execute actions in external systems using tool calling (function calling) — for example, booking appointments, querying APIs, or updating database entries. * Recommended Models: High-performance models like ew/minimax27 or ew/deepseek32, which reliably handle complex logic and tool calls.

4. Code Assistance and Development

Integrate our models into your internal development processes to generate code snippets, automate code reviews, write unit tests, or analyze legacy code. * Recommended Models: Specially trained coding models or all-rounders like ew/deepseek32. * Practical Guide: Learn how to set up the terminal-based OpenCode Agent and use it with our LLMaaS Gateway.

5. Multilingual Content & Translation

Translate documents, user interfaces, or support interactions accurately and context-sensitively, specifically optimized for the DACH region (Swiss High German, German, French, Italian).

Getting Started with Implementation

  1. API Key & Access: If you haven't already, request a virtual key through the Cloud Service Portal or our support team.
  2. Model Selection: Choose the appropriate model for your requirements. In general:
    • Use top-tier models for complex tasks (reasoning, agents, RAG).
    • Use smaller models for simple, repetitive tasks such as classification to minimize costs and inference times.
  3. Integration: Use the OpenAI SDK in your preferred programming language (Python, JS/TS, Go) and simply point it to the api_base and use your API key.

Customized Consultation

Are you planning a specific project or do you need assistance with the architecture of your RAG pipeline? Our support and solutions team will be happy to assist you with the design and implementation.