LLM Fine-Tuning & Custom Models
We fine-tune and train custom language models optimized for your domain, data, and performance requirements.
- Supervised fine-tuning, RLHF, and instruction tuning
- Dataset curation, cleaning, and augmentation
- Evaluation benchmarks and A/B testing
What we do
We help companies move beyond generic LLMs by fine-tuning models for specific domains, tasks, and quality standards. This includes dataset preparation, training strategy, evaluation, and deployment — with clear cost and performance tradeoffs.
Use cases
Representative ways teams deploy this capability in production.
Domain-specific assistant
Problem: Generic LLMs lack accuracy for specialized fields.
Solution: Fine-tuned model on domain data with evaluation benchmarks.
Result: Higher accuracy, consistent terminology, lower hallucination.
Code generation for internal tools
Problem: Teams need code completion trained on internal APIs and patterns.
Solution: Fine-tuned code model on internal repos with safety checks.
Result: Faster development, consistent code style, fewer errors.
Content generation at scale
Problem: Marketing teams need brand-consistent content.
Solution: Fine-tuned model on brand guidelines and approved examples.
Result: On-brand output with minimal editing.
Classification & extraction
Problem: Manual labeling and extraction is slow and inconsistent.
Solution: Fine-tuned classifier on labeled examples with active learning.
Result: Higher throughput and consistent quality.
How it works
- Data assessment & curation — Audit existing data, define quality criteria, and build training sets.
- Training strategy — Select base model, training approach (SFT, RLHF, LoRA), and hyperparameters.
- Training & iteration — Run training with checkpointing, validation, and early stopping.
- Evaluation & benchmarking — Test against domain benchmarks, edge cases, and production scenarios.
- Deployment & monitoring — Serve the model with versioning, A/B testing, and performance tracking.
Architecture & technology
We design fine-tuning pipelines with data versioning, experiment tracking, and automated evaluation — so you can iterate on model quality with confidence and control costs.
Why work with us
- Experience with production fine-tuning (not just demos)
- Rigorous evaluation methodology
- Cost-aware training strategies (LoRA, QLoRA, distillation)
- Clear documentation and reproducibility
Let's discuss your project
Technical conversation first. We'll map the shortest path from your goal to a reliable production system.
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FAQ
When should I fine-tune vs use RAG?
Fine-tune for style, format, and domain knowledge. Use RAG for dynamic data and citation. Often both are combined.
How much data do I need?
Depends on the task. Classification can work with hundreds of examples; complex generation may need thousands.
Which base models do you work with?
We work with open-source (Llama, Mistral, Qwen) and commercial APIs (OpenAI, Anthropic) depending on requirements.
How do you handle data privacy?
Training can run on private infrastructure. We support on-premise and VPC deployments.
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