The Problem
Generic models give generic results. GPT-4 doesn't know your industry terminology. Claude hasn't seen your internal documentation. Every prompt requires context that should just be built-in.
You could use bigger models, but costs add up fast. You could add more examples to prompts, but you're hitting token limits. And the responses still feel... generic.
What Fine-Tuning Solves
A fine-tuned model learns your domain. Research shows that smaller, specialized models often outperform larger generic ones on specific tasks—at a fraction of the cost.
The benefits:
- Better accuracy: The model speaks your language, literally
- Lower costs: Smaller models, fewer tokens, cheaper inference
- Faster responses: Less computation means lower latency
- Data privacy: Training data stays under your control
The result: An AI that feels like it was built for your use case—because it was.
How We Help
We handle the full fine-tuning pipeline:
- Data Preparation: Structure your data for optimal training results
- Model Selection: Choose the right base model for your constraints
- Training & Evaluation: Fine-tune and rigorously test against your benchmarks
- Deployment: Integrate the trained model into your existing systems
You provide the domain knowledge. We turn it into a specialized AI.