Custom AI Model Training & Fine-Tuning
Make AI work for your domain. We fine-tune open-source LLMs on your data to create smaller, faster, cheaper models that outperform generic ones, then deploy them in your own infrastructure.
Model Training & Fine-Tuning Capabilities
End-to-end: dataset preparation, training, evaluation, and private deployment.
Dataset Curation & Prep
We clean, label, format, and augment your data (including synthetic data generation) to build a high-quality training set.
LoRA & QLoRA Fine-Tuning
Efficient parameter-tuning that adapts large models to your domain at a fraction of full-training cost and time.
Domain & Task Adaptation
Teach models your industry vocabulary, output formats, and specialized tasks for legal, medical, finance, and more.
Evaluation & Benchmarking
Rigorous before/after evaluation against your metrics so improvements are measured and provable.
Private & On-Prem Deployment
Serve fine-tuned models in your own cloud or on-premise with vLLM, TGI, or Ollama. You own the weights.
MLOps & Retraining Pipelines
Automated pipelines for versioning, monitoring, and periodic retraining as your data and needs evolve.
Fine-Tuning Use Cases
Our Fine-Tuning Process
Assess & Baseline
Define the task, build an eval set, and benchmark the base model
Prepare Dataset
Curate, clean, format, and augment your training data
Train & Evaluate
Fine-tune with LoRA/QLoRA and compare results against the baseline
Deploy & Maintain
Serve in your infrastructure with monitoring and a retraining pipeline
Fine-Tuning Tech Stack
Explore More AI Services
Frequently Asked Questions
Fine-tuning takes a pre-trained foundation model (like Llama 3, Mistral, or GPT) and continues training it on your specific data so it learns your domain vocabulary, tone, formats, and tasks. The result is a smaller, faster, cheaper model that often outperforms a much larger general-purpose model on your particular use case, while keeping your data and behaviour under your control.
Start with prompt engineering and RAG, since they are cheaper and faster. Fine-tune when you need: a consistent output style or format the base model struggles with, a specialized task with lots of examples, lower latency and cost at high volume, or an on-premise model for data privacy. Often the best solution combines a fine-tuned model with RAG for grounding.
It depends on the task. Instruction or style fine-tuning can show strong results with 500–2,000 high-quality examples. More complex domain adaptation may need 10,000+. Quality matters far more than quantity. We help you curate, clean, and format your dataset, and can generate synthetic data to fill gaps.
We fine-tune open-source models (Llama 3, Mistral, Mixtral, Phi, Gemma, and others) using techniques like LoRA and QLoRA for efficient training. We also support managed fine-tuning on OpenAI and other hosted platforms when that fits better. We recommend the right base model and method for your accuracy, cost, and privacy requirements.
Yes. For data privacy and cost control, we deploy fine-tuned open models in your own cloud (AWS, GCP, Azure) or on-premise using vLLM, TGI, or Ollama for serving. You own the model weights and the infrastructure, so there are no per-token vendor fees and no data leaves your environment.
We build an evaluation set before training and benchmark the fine-tuned model against the base model and your current solution. We track task-specific metrics (accuracy, format adherence, latency, cost per request) and run blind quality comparisons. You get a clear before/after report so the improvement is provable, not just claimed.
Ready to Fine-Tune Your Own AI Model?
Share your use case and data situation. We will tell you honestly whether fine-tuning is the right move and, if so, map out the dataset, method, and deployment plan.