Fine-Tuning for Domain Performance

Adapt foundation models to your domain, terminology, and task requirements for production-grade performance.

Choosing the Right Fine-Tuning Approach

We select the method that balances performance, cost, and your data constraints.

Highest Performance
Full Fine-Tuning
Maximum performance
High compute
10K+ examples
Best Value
LoRA / QLoRA
Cost-efficient adaptation
Low compute
1K+ examples
Task Alignment
Instruction Tuning
Task-specific behavior
Medium compute
5K+ examples
Quality Alignment
RLHF / DPO
Human preference alignment
High compute
Preference pairs

What we deliver

Foundation models are powerful starting points, but they are not optimized for your specific domain, terminology, or task requirements. Fine-tuning adapts these models to your context — improving accuracy, reducing hallucinations, and aligning outputs to your quality standards.

We select the right fine-tuning approach for your use case and constraints: full fine-tuning for maximum performance, parameter-efficient methods (LoRA, QLoRA) for cost efficiency, or RLHF and preference optimization for alignment to human judgment.

Key deliverables

  • Model selection and baseline performance benchmarking
  • Supervised fine-tuning on domain-specific datasets
  • Instruction tuning for task-specific behavior
  • Parameter-efficient fine-tuning with LoRA and QLoRA
  • RLHF and Direct Preference Optimization (DPO)
  • Post-training evaluation and regression testing
30–60%
Accuracy improvement over base model
90%
Cost reduction with LoRA vs full fine-tuning
70%
Hallucination reduction with domain fine-tuning
2 wks
Typical time from data to fine-tuned model

Real-Life Use Cases

Fine-tuning delivering domain-specific performance that generic models can't match.

Legal

Legal Document Summarization

A law firm fine-tuned an LLM on 50,000 legal briefs and case summaries. The fine-tuned model produces summaries that senior partners rated as "publication-ready" 78% of the time, vs 23% for the base model. Hallucination of case citations dropped from 31% to 4%.

Citation hallucination: 31% → 4%
Pharma

Clinical Trial Report Generation

A pharmaceutical company fine-tuned a model on their clinical trial report corpus. The model now drafts regulatory submission sections that require 40% less editing than base model output. Regulatory team capacity increased by 35%.

40% less editing required on AI drafts
Telecom

Customer Support Fine-Tuning

A telecom provider fine-tuned a support model on 200K resolved tickets. The fine-tuned model resolves 61% of tickets without escalation, vs 34% for the base model. Customer satisfaction scores for AI-handled tickets improved from 3.2 to 4.1/5.

Self-resolution rate: 34% → 61%
Finance

Financial Report Analysis

An investment firm fine-tuned a model on 10 years of earnings reports and analyst notes. The model now extracts key financial metrics and generates investment thesis summaries with 89% accuracy on held-out test data — matching junior analyst performance.

89% accuracy matching junior analyst performance

Make the model work for your domain

We'll select the right fine-tuning approach and deliver a model that performs at production quality in your context.

Fine-Tune Your Model