Choosing Models on Evidence, Not Intuition
At Beyonetix, LLM consulting starts with measurement. We select language models against four criteria: license terms, available GPU hardware, budget, and the actual task. A routing or classification job often runs well on a smaller model such as Mistral or Qwen, while demanding reasoning work calls for larger open-weight models. Every candidate runs under vLLM on our own servers in Germany. Which model wins is decided by your data, not by a vendor spec sheet.
To make that decision auditable, we build a reproducible eval harness against your real questions and source documents. We score with RAGAS metrics: Faithfulness checks whether an answer is supported by the retrieved context, Answer Relevancy measures fit to the question, and Context Precision rates the quality of retrieval itself. Because LLM output is non-deterministic, each evaluation runs several times, and we report the variance rather than a single flattering number.
The measurements drive the architecture. Whether citation-grounded RAG or fine-tuning is the right path depends on how often your knowledge changes and whether source citations are mandatory. Our assessment covers:
- Prompt design, tool calling, and structured JSON output
- Guardrails against hallucination and prompt injection
- PageIndex and knowledge graph as retrieval reinforcement
- Total cost of ownership: cloud tokens versus self-hosting over the expected lifetime
- The GDPR and EU AI Act framework we implement on your behalf
The result is a recommendation you can verify against the measurements yourself.