Language Models That Speak Your Business.
Trinos fine-tunes models around your domain, documents and terminology, and tells you honestly when fine-tuning isn't the right answer.
What is LLM Fine Tuning?
LLM Fine Tuning adapts a base language model using carefully prepared examples from your domain, so it learns your terminology, answer style, document formats and task patterns. It's most valuable when a general model isn't consistent enough for repeatable, specialist enterprise work.
What we deliver
Dataset Curation & Cleaning
Proprietary examples, prompts and outputs prepared for safe, useful training, noise removed, formats aligned, data matched to the real task.
Instruction Fine Tuning
Models trained to follow your instructions, output formats and industry language consistently.
Small Language Model Strategy
Compact, domain-specific models where they improve cost, speed and control for focused tasks.
Evaluation & Benchmarking
Testing for accuracy, hallucination risk, latency and cost, so you can choose between fine-tuning, RAG or a hybrid with evidence.
Case Study & results
Insurance & Legal Review
Better classification, summarization and document analysis from models trained on domain terminology, supporting review of policies, contracts and claims.
Internal Knowledge & Support
Models that follow company style and approved formats, giving HR, IT and operations teams consistent answers from internal information.
More reliable, cost-efficient AI for repeatable tasks where a general model isn't precise enough.
Built on a modern AI toolchain, applied across industries
The toolchain we work with
- Hugging Face
- PyTorch
- LoRA
- PEFT
- AWS SageMaker
- Azure Machine Learning
- Evaluation frameworks
Where this service is applied
- Insurance
- Legal
- Healthcare
- Manufacturing
- Financial services
- Enterprise technology
Frequently asked questions
No. Many use cases work well with retrieval, strong prompting or a general model. Trinos recommends fine tuning only when it improves accuracy, consistency or cost for a clearly defined task.
It depends on the task and the quality of the examples. Narrow use cases may begin with hundreds of high quality examples while complex tasks need larger, cleaner and more representative datasets.
The model can run in your cloud, a private instance or a managed inference endpoint based on your security and governance needs. The deployment choice depends on data sensitivity, performance requirements and operational control.
Build an AI Model That Speaks Your Domain
We'll assess your data, task complexity and options before recommending fine-tuning, RAG or a hybrid approach.