LLM Fine-Tuning Services
A model that speaks your domain, not the internet’s.
Fine-tuning language models on your domain-specific data for better accuracy, on-brand behaviour and lower inference cost on the tasks you run most.
When prompting and retrieval hit their limit, fine-tuning teaches a model your task, tone and terminology - often at a fraction of the running cost.
We fine-tune open and commercial models on your data so they follow your formats, use your vocabulary and behave consistently on high-volume, specialised tasks. Efficient methods like LoRA and QLoRA make this practical.
What we build
Fine-tuning done with discipline
Data quality and evaluation are what separate a useful fine-tune from a wasted one.
Use-case assessment
We determine whether fine-tuning, RAG or prompting best fits your goal - and often combine them.
Data curation
We build clean, diverse, correctly-formatted training sets, the single biggest driver of model quality.
Efficient tuning
LoRA / QLoRA and PEFT methods to adapt models cost-effectively, with swappable task-specific adapters.
Domain adaptation
Models that reliably follow your formats, tone and terminology on specialised tasks.
Evaluation framework
Held-out test sets and task metrics so improvements are measured, not assumed.
Deploy & serve
Efficient serving of fine-tuned models or adapters, self-hosted or on managed infrastructure.
How we deliver
A path from idea to production
A pragmatic engagement model that de-risks adoption and gets a working system in front of your users fast.
- 01
Fit assessment
We validate that fine-tuning is the right lever and define success metrics up front.
- 02
Data preparation
We curate, clean and format training data - the work that determines the outcome.
- 03
Train & evaluate
We run efficient fine-tuning and measure against a held-out set and task-specific metrics.
- 04
Deploy & iterate
We serve the model or adapter and re-tune as new data and requirements arrive.
Tools & stack
Technologies we work with
We stay model- and vendor-flexible, choosing the stack that fits your data, budget, and compliance needs.
- LoRA / QLoRA
- Hugging Face TRL
- Unsloth
- Axolotl
- Together AI
- Fireworks AI
- Predibase
- Google Vertex AI
- Amazon Bedrock
- Llama / Mistral / Gemma
Where it fits
Use cases & industries
For high-volume, specialised tasks where consistency and cost matter.
Domain classification
Reliable categorisation in your industry's taxonomy and language.
Structured extraction
Consistent extraction into your exact schemas and formats.
On-brand generation
Copy and responses that match your voice and style every time.
Specialised assistants
Models tuned to your product, jargon and workflows.
Cost reduction
Smaller fine-tuned models replacing costly general-purpose calls at scale.
Edge / private models
Own and run tuned open models entirely on your infrastructure.
Common questions
If you need current or frequently-changing facts, RAG is usually better. Fine-tuning is for teaching a model a task, format or style. Many production systems use both - we'll help you decide.
It depends on the task, but often hundreds to a few thousand high-quality examples. Data quality matters far more than raw volume.
Yes. We can fine-tune open-weight models like Llama or Mistral and deploy them entirely on your own infrastructure.
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