Fine-tune open-source LLMs to speak your industry's language. Trained on your infrastructure, owned by you, with zero data leaving your control.
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Fine-tuning and RAG solve different problems. Understanding when to use each, or both, is critical for AI success. Here is how they compare.
| Aspect | RAG | Fine-Tuning |
|---|---|---|
| Best for | Querying existing document bases | Domain-specific language and behaviour |
| Data requirement | Works with existing documents as-is | Needs curated training examples (100+) |
| Knowledge updates | Instant: add or remove documents | Requires retraining with new data |
| Cost per query | Higher (retrieval + generation) | Lower (no retrieval step needed) |
| Hallucination risk | Low: grounded in retrieved sources | Medium: depends on training quality |
| Setup time | 2-4 weeks typical | 4-8 weeks typical |
Not sure which approach fits your use case? We help you decide during our free initial consultation. Many production systems combine both for optimal results.
We work with your team to identify, clean, and structure your training data. This includes domain-specific documents, examples of ideal outputs, and edge cases. Data quality determines model quality, so we invest heavily in this step.
We fine-tune open-source foundation models (Llama, Mistral, or similar) on your prepared dataset. Training runs on your infrastructure or our German-hosted GPU servers. Multiple training runs with different hyperparameters to find the optimal configuration.
Rigorous testing against your quality benchmarks. We evaluate accuracy, hallucination rates, response quality, and domain coverage. Your subject matter experts validate outputs before we proceed to deployment.
Production deployment on your chosen infrastructure with monitoring, logging, and version management. We set up model serving, API endpoints, and integration with your existing applications.
We fine-tune open-source foundation models, including Llama, Mistral, and others, so your proprietary data never leaves your environment. No per-token fees, no data sharing with model providers, full ownership of the resulting model weights.
Llama 3
Meta's open foundation model
Mistral / Mixtral
European-built, high performance
Qwen
Strong multilingual capabilities
Custom Selection
We evaluate the best model for your task
Models trained on financial terminology, risk frameworks, and regulatory language for banking and insurance.
Learn moreCurriculum-aligned models for adaptive learning, exam preparation, and pedagogically sound AI tutoring.
Learn moreDomain-specific models trained on technical documentation, product specifications, and quality standards.
Learn moreFine-tuning is the right choice when you need the model to understand domain-specific language, follow specific output formats, or exhibit consistent behaviour patterns that cannot be achieved through prompting alone. Examples include medical report generation, legal clause drafting, or technical documentation in specialized fields. If you primarily need to query existing documents, RAG is usually more cost-effective. Many production systems combine both approaches.
With modern techniques like LoRA, meaningful improvements are possible with as few as 100 to 500 high-quality examples. For specialized tasks, 1,000 to 5,000 examples typically produce strong results. The quality of training data matters far more than quantity. 200 carefully curated examples often outperform 10,000 noisy ones. We help you identify and prepare the right data during the data preparation phase.
We work primarily with open-source models: Llama 3 (Meta), Mistral, Mixtral, and similar. These models offer strong baseline performance, full transparency, and can be deployed without any data leaving your infrastructure. We also fine-tune models on Azure OpenAI for organisations that prefer managed infrastructure with EU data residency.
Yes. Training runs on infrastructure you control: your own servers, our German-hosted GPU infrastructure on Hetzner, or Azure EU regions. Your training data is never uploaded to third-party platforms or model providers. After training, you own the model weights and can deploy them independently.
We use a combination of automated metrics (perplexity, BLEU/ROUGE scores, task-specific benchmarks) and human evaluation by your domain experts. We establish baseline performance before training, measure improvements on a held-out test set, and run A/B comparisons against the base model. You get a detailed evaluation report with every training run.
Free 30-minute strategy call with Gerrit: no sales pitch, just a concrete roadmap for your business.