Custom fine-tuned models that outperform GPT-4 on your specific tasks — at 1/10th the inference cost. We handle data preparation, technique selection, training, evaluation, and production deployment.
Generic LLMs hallucinate on domain-specific content — legal, medical, financial, automotive terminology
Prompt engineering workarounds add latency, cost, and brittleness that compound at scale
Cloud API costs grow 5–10× faster than usage as you move from pilot to production
Vendor dependency: one pricing change or API deprecation breaks your entire AI pipeline
Compliance teams won't approve models that send proprietary data to third-party APIs
We follow a rigorous 6-stage methodology from task definition to production deployment.
Define the target task precisely, audit your existing data, identify gaps, and design a data collection strategy.
Benchmark the best-fit base model on your actual use case to establish a performance floor before any training.
Choose between LoRA, QLoRA, full fine-tuning, DPO, or GRPO based on your data volume, hardware, and quality requirements.
Execute training with Unsloth + Axolotl or torchtune on your infrastructure or cloud — with full experiment tracking.
Benchmark on MMLU, MT-Bench, and custom domain evals. Red-team for failure modes before deployment.
Export to GGUF/ONNX, deploy via Ollama or vLLM, set up monitoring and A/B testing against baseline.
Every fine-tuning engagement follows our DEPLOY framework: Define task precisely, Evaluate baseline, select optimal technique, Prepare data, Loop through training cycles, Operationise in production, Yield measured improvements.
You have proprietary document corpora that generic models mishandle, you're in a regulated industry that requires data sovereignty, your AI inference bill exceeds €5K/month and is growing, or you have 50K+ domain-specific examples waiting to be turned into a competitive moat.
For LoRA fine-tuning, you can see meaningful improvement with as few as 1,000 high-quality examples. Production-grade fine-tuning typically uses 10K–100K examples. We audit your existing data and advise on collection if gaps exist.
QLoRA can fine-tune a 7B model on a single 24GB GPU (RTX 3090/4090). For 70B models, we use multi-GPU setups or cloud compute (A100/H100). We can work with your existing hardware or procure cloud compute for the training run.
LoRA is our default — it trains only adapter layers, is fast, and preserves base model knowledge. QLoRA adds 4-bit quantization, reducing VRAM requirements by 75% at minimal accuracy cost. Full fine-tuning is reserved for cases where you're significantly changing model behaviour, not just domain adapting.
Fine-tuning and RAG are complementary, not competing. RAG is ideal for retrieving up-to-date facts from large document stores. Fine-tuning excels at teaching the model style, format, domain terminology, and reasoning patterns. Most production systems use both.
By default, we train on your infrastructure or a cloud environment you control — your data never leaves your perimeter. For clients without GPU infrastructure, we can provision cloud compute (AWS, GCP, Azure) in your account.
It depends on your requirements. Llama 3.3 70B for maximum quality, Mistral Nemo 12B for EU-sovereign deployments, Phi-4-mini 3.8B for edge deployment. We benchmark 3–4 candidates before committing to training.
Let's discuss how this service can address your specific challenges and drive real results.