Custom Model Training
Generic foundation models plateau quickly. We fine-tune LLMs and specialised models on your proprietary data to unlock domain accuracy that no off-the-shelf API can match.
Everything included
From selecting the right base model to serving the fine-tuned result via an API you own — we cover the complete training lifecycle with no gaps.
- Foundation model selection (GPT, LLaMA, Mistral, Gemma, etc.)
- Proprietary dataset collection and annotation pipelines
- Full fine-tuning, LoRA, and QLoRA training runs
- RLHF and preference optimisation (DPO, PPO)
- Rigorous evaluation: benchmarks, human evals, red-teaming
- Quantisation (GGUF, GPTQ, AWQ) for efficient inference
- Model merging and ensemble techniques
- Hosted inference API with OpenAI-compatible endpoints
- Ongoing retraining as your data grows
- Private deployment — your model, your infrastructure
Tech Stack
Why fine-tune?
- 3–10×Better accuracy on domain tasks vs generic APIs
- 90%Cost reduction by running smaller, specialised models
- 100%Data privacy — your training data never leaves your cloud
- <1sInference latency with optimised quantised models
Real-world use cases
Specialised models beat generic APIs in every vertical that matters.
Domain-Specific Chatbots
Fine-tune LLMs on your documentation, FAQs, and support tickets to create expert assistants that know your product inside out.
Code Generation
Train models on your proprietary codebase and style guides to generate code that follows your conventions automatically.
Legal & Compliance
Models trained on legal corpora that understand jurisdiction-specific terminology and surface accurate references.
Medical / Clinical NLP
Extract diagnoses, medications, and clinical notes from unstructured records with HIPAA-compliant fine-tuned models.
Multilingual Support
Train models for low-resource languages or regional dialects that generic APIs can't handle reliably.
Sentiment & Brand Monitoring
Fine-tune classifiers on industry-specific sentiment to get signal that generic models miss entirely.
Own a model that knows your domain
Bring your dataset (or let us help build one) and we'll train a model that outperforms the generic alternatives — and belongs to you entirely.