The Data Engine
A model is only as good as the data it feeds on. 80% of our fine-tuning process is actually data engineering.
Noise Filtration
We remove duplicates, PI (Personal Information), and low-quality tokens from your dataset.
Synthetic Data Generation
Don‘t have enough data? We use large teacher models (like Gemini 1.5 Pro) to generate high-quality training examples for your smaller student model.
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Fine-Tuning vs RAG
Do you need a model that *knows* things, or a model that *acts* a certain way? We help you decide.
Use RAG when...
- You need 100% factual accuracy.
- Your data changes frequently (daily/weekly).
- You need citations for every answer.
Use Fine-Tuning when...
- You need a specific “voice“ or speaking style.
- You need to reduce latency/cost (act like GPT-4, cost like GPT-3.5).
- You need consistent, complex output formatting (e.g. valid SQL).
The Fine-Tuning Process
We take raw data and turn it into a specialized intelligence asset.
Data Preparation
The most critical step. We clean, de-duplicate, and format your unstructured data into high-quality instruction-response pairs.
LoRA & QLoRA
We use Parameter-Efficient Fine-Tuning (PEFT) techniques to adapt massive models like Llama 3 or Mistral on consumer hardware if needed.
Evaluation Framework
We do not guess. We run benchmarks against a holdout set to ensure the model learned new behaviors without catastrophic forgetting.
Model Distillation
Shrink a GPT-4 level teacher model into a fast, cheap student model that runs on your own servers.
Private Deployment
We deploy the final weights to your AWS Bedrock, SageMaker, or private vLLM container.
Continuous Training
Set up pipelines to re-train the model weekly as your users generate new data.