Generic LLMs are general-purpose. Fine-tuned models are competitive weapons. We help you collect, curate, and use your proprietary data to train models that speak your industry's language, follow your brand's style, and make decisions aligned with your business logic — at a fraction of the inference cost of the frontier models.
Every engagement is built around your specific goals — here's the full scope of what we bring to the table.
Collection, cleaning, deduplication, and quality filtering of your proprietary datasets. The quality of your fine-tune is only as good as the data that goes in.
Full fine-tuning and parameter-efficient methods (LoRA, QLoRA) on base models like Llama 3, Mistral, and Phi-3 — or via the OpenAI and Anthropic fine-tuning APIs.
Reinforcement learning from human feedback and Direct Preference Optimisation (DPO) to align model behaviour with your quality standards.
Domain-specific evaluation datasets, automated benchmarks, and human eval processes to measure whether your fine-tune actually outperforms the base model on your tasks.
Deploying your fine-tuned model on cost-efficient GPU infrastructure with vLLM or TGI inference servers — up to 10× cheaper per token vs. frontier API calls.
Feedback loops, active learning pipelines, and periodic re-training so your model improves as you accumulate more data from production.
A structured, transparent workflow so you always know what's happening and what comes next.
We assess your existing data sources — support tickets, documents, code, conversations — for volume, quality, and fine-tuning suitability.
Define the specific behaviour you want to improve, create a benchmark dataset, and measure the base model's current performance.
Formatting data into training pairs (prompt → completion), applying quality filters, and splitting into train/validation/test sets.
Iterative training with hyperparameter optimisation. Multiple checkpoints evaluated against the benchmark to select the best-performing model.
Model served on your infrastructure or hosted API, with latency monitoring, cost tracking, and drift detection in production.
We use best-in-class tools and frameworks — chosen for your project, not for familiarity.
Everything you need to go live and grow — documented, handed over, and supported.
For supervised fine-tuning, 500–5,000 high-quality examples often produce strong results. For behaviour alignment, even 100 carefully crafted preference pairs can make a meaningful difference.
Use RAG when your knowledge base changes frequently. Use fine-tuning to change model behaviour, style, or reasoning patterns. Many production systems use both together.
You do. The trained weights are your intellectual property and are delivered to your infrastructure. We sign an IP assignment agreement before any training begins.
Yes. We can run the entire training pipeline in your cloud environment (AWS, GCP, Azure) so your data never leaves your infrastructure.
Llama 3 (8B, 70B), Mistral 7B, Phi-3, Qwen 2, and most open-weight models on Hugging Face. For hosted APIs, OpenAI GPT-4o mini and GPT-3.5 fine-tuning is also available.
Book a free 30-minute discovery call. No pitch, no pressure — just a genuine conversation about what you need.
