Fine-tuning

Model Fine-tuning

Fine-tuning pre-trained and foundation models on your proprietary data — for higher accuracy and lower inference cost than generic API calls.

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45+
Fine-Tuning Projects Delivered
40%
Avg Accuracy Improvement vs Base Model
50%
Avg Inference Cost Reduction
10+
Foundation Models Supported
Fine-tuning

Better Accuracy, Lower Cost, Than Generic Model Calls

Generic foundation models are impressively general but rarely optimal for your specific domain and task. Fine-tuning on your proprietary data typically improves accuracy meaningfully while allowing you to use a smaller, cheaper model than the largest generic API — a double win on quality and cost.

  • Fine-tuning strategy for your specific domain and task
  • Training data curation and preparation for fine-tuning
  • LoRA and parameter-efficient fine-tuning for cost control
  • Base model selection and comparison for your use case
  • Evaluation against both base model and business benchmarks
  • Ongoing fine-tuning refresh as your data and needs evolve
Our Approach

Efficient Fine-Tuning, Not Brute-Force Retraining

Full model retraining is rarely necessary or cost-effective. We use parameter-efficient techniques like LoRA where appropriate, dramatically reducing training cost and time while capturing most of the accuracy benefit of full fine-tuning.

Domain-Specific Accuracy

Meaningful accuracy gains on your specific tasks versus generic base models.

Lower Inference Cost

Smaller fine-tuned models often outperform larger generic ones at lower cost.

Efficient Techniques

Parameter-efficient fine-tuning (LoRA) for faster, cheaper training runs.

Ongoing Refresh

Scheduled fine-tuning updates as your data and requirements evolve.

Delivery Process

From Base Model to Domain-Tuned Performance

We select the right base model and fine-tuning approach for your data volume and budget, then validate improvement against clear benchmarks.

  • Curate and prepare high-quality training data for fine-tuning
  • Select base model and fine-tuning approach (full vs. parameter-efficient)
  • Run fine-tuning experiments and evaluate against base model performance
  • Validate against real-world business benchmarks and edge cases
  • Deploy and establish a refresh cadence as data evolves
FAQs

Frequently Asked Questions

Fine-tuning can show meaningful improvement with as few as a few hundred high-quality examples for narrow tasks, though more data generally helps. We assess your available data and advise on realistic expectations during scoping.

We work with open-source models (Llama, Mistral) as well as fine-tuning APIs offered by OpenAI and other providers, selecting based on your data privacy requirements, cost tolerance, and performance needs.

Prompt engineering shapes model behaviour through instructions at inference time with no retraining; fine-tuning actually updates model weights on your data, typically yielding more consistent and accurate results for well-defined, repeated tasks.

Often yes — a well fine-tuned smaller model can match or beat a larger generic model's accuracy on your specific task, at a meaningfully lower per-request inference cost.

Get More Accuracy and Lower Cost From Your AI Models

Book a free consultation to discuss fine-tuning for your use case.

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