Make AI models that understand YOUR business. We fine-tune LLMs on your domain data — medical terminology, legal language, financial regulations, product catalogs, or any specialized knowledge. Better accuracy, lower costs, consistent outputs. From data preparation to deployed custom model.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
The quality of fine-tuning depends on data quality. We help you collect, clean, format, and augment training examples. Instruction-response pairs, preference data for DPO/RLHF, and synthetic data generation to fill gaps in your dataset.
Parameter-efficient fine-tuning that adapts large models on modest hardware. LoRA for full precision, QLoRA for 4-bit quantized training. Fine-tune 70B parameter models on a single A100 GPU. Merge adapters for production deployment.
Full parameter training for maximum model adaptation. Multi-GPU training with DeepSpeed and FSDP. When your domain is significantly different from the base model training data and LoRA is not enough.
Fine-tune GPT-4o and GPT-4o-mini via OpenAI API. Training data preparation in JSONL format, hyperparameter optimization, evaluation metrics, and A/B testing against base models.
Rigorous evaluation against your domain benchmarks. Automated evaluation suites, human evaluation frameworks, A/B testing, and regression detection. Know exactly how much better your fine-tuned model performs.
Deploy fine-tuned models with vLLM, TGI, or Ollama. GPU-optimized inference, model quantization (GGUF, GPTQ, AWQ), auto-scaling, and API endpoints. On-premise or cloud deployment with monitoring.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
The quality of fine-tuning depends on data quality. We help you collect, clean, format, and augment training examples. Instruction-response pairs, preference data for DPO/RLHF, and synthetic data generation to fill gaps in your dataset.
Parameter-efficient fine-tuning that adapts large models on modest hardware. LoRA for full precision, QLoRA for 4-bit quantized training. Fine-tune 70B parameter models on a single A100 GPU. Merge adapters for production deployment.
Full parameter training for maximum model adaptation. Multi-GPU training with DeepSpeed and FSDP. When your domain is significantly different from the base model training data and LoRA is not enough.
Fine-tune GPT-4o and GPT-4o-mini via OpenAI API. Training data preparation in JSONL format, hyperparameter optimization, evaluation metrics, and A/B testing against base models.
FAQ
Tell us about your domain and data — we'll assess whether fine-tuning is the right approach and design a training pipeline that delivers measurable accuracy improvements.