Model Finetuning
We improve model performance on your tasks through careful finetuning, from data curation and training to evaluation and deployment.
General-purpose models are a great start, but your domain, tone and tasks are specific. Finetuning can make models more accurate, faster and cheaper for exactly the work you need.
Problems we solve
- Off-the-shelf models that miss your domain's terminology and edge cases
- Prompts that have grown long, fragile and expensive
- Latency or cost that rules out the largest models in production
- Finetuning attempts that didn't clearly improve anything
How an engagement works
- Baseline: we measure current performance on tasks and data that reflect real use.
- Curate data: we build and clean training data, including synthetic data where it helps.
- Train and compare: we finetune candidate models and compare them rigorously against the baseline.
- Deploy: we ship the winning model with monitoring and a plan for retraining.
What you get
- A finetuned model with measured gains on your own tasks
- A reproducible training pipeline and versioned datasets
- Side-by-side comparisons of quality, latency and cost
- Deployment and monitoring so performance holds up over time