LLM Fine-Tuning
Your model. Your domain. Your competitive advantage.
Off-the-shelf models are trained on the internet. Your business isn't the internet.
Domain-specific LLM fine-tuning for enterprises that need accuracy, consistency, and proprietary capability. We build the training sets, evaluation harnesses, and deployment pipelines that turn a general model into your model.
Base model vs. fine-tuned model
The difference between a model that generates plausible text and one that generates accurate, domain-specific output your team can trust.
Off-the-shelf LLM
- Generic responses that miss domain terminology
- Hallucinations on proprietary processes and policies
- Inconsistent tone and formatting across outputs
- No understanding of your internal decision logic
- Requires constant prompt engineering to stay on track
AurvikAI fine-tuned model
- Uses your exact terminology and classification systems
- Grounded in your documentation and decision patterns
- Consistent output format matching your workflows
- Encodes institutional knowledge into model weights
- Reliable enough to deploy without constant supervision
Fine-tuning approaches we deploy
The right method depends on your data volume, target behaviour, and infrastructure constraints.
Complete weight updates for maximum domain adaptation when data volume and compute allow.
Building and validating training sets from your proprietary documentation, decision logs, and domain content.
Custom evaluation framework measuring domain accuracy, regression on general capabilities, and business-specific metrics.
Multi-GPU training infrastructure with checkpoint management and experiment tracking.
The 80/20 of fine-tuning
Fine-tuning quality is 80% data quality.
Most fine-tuning failures trace back to training data — insufficient volume, poor diversity, inconsistent labelling, or data that doesn't represent the target behaviour. We spend more time on data than on model selection because that's where the leverage is.
of fine-tuning outcomes determined by data quality
AurvikAI delivery analysis, 2019–2025
Data audit
We profile your existing data for relevance, diversity, quality, and coverage before writing any training code.
Annotation pipeline
Custom annotation workflows with domain expert reviewers and inter-annotator agreement measurement.
Synthetic augmentation
Generating high-quality synthetic training examples for rare edge cases and underrepresented categories.
Quality validation
Statistical quality checks on every training batch — consistency scoring, deduplication, and contamination detection.
Where we've fine-tuned models
Real production deployments across regulated and high-stakes domains.
Clinical documentation AI
Clinicians reviewing and approving AI-generated notes in under 60 seconds, saving 3.5 hours per shift. Trained on 50,000+ de-identified clinical documents with NHS terminology.
saved per clinician per day
AurvikAI model evaluation — every fine-tuning project ships with a rigorous evaluation framework before training begins.
Common questions about LLM fine-tuning
Straight answers from engineers who have shipped fine-tuned models in production.
Depends on the method and target behaviour. Instruction tuning can work with 1,000–5,000 high-quality examples. Full fine-tuning for domain adaptation typically needs 10,000–100,000+ examples. LoRA sits in between. We assess data sufficiency in the audit phase and design collection strategies for gaps.
INSIGHTS
Thinking worth reading
Ready to fine-tune a model on your domain?
Let's start with a conversation about your data, your use case, and what fine-tuning could unlock for your business.