Custom LLM solutions and fine-tuning
Fine-tuning is the process of further training a pre-trained large language model on your own examples so it follows your domain vocabulary, output format and tone more reliably. Visions selects, fine-tunes, evaluates and deploys language models, including private self-hosted models for sensitive data.
Overview
Custom LLM Solutions & Fine-Tuning explained
General-purpose language models are powerful, but they do not know your terminology, style or output formats, and some organisations cannot send data to a third-party API. Custom LLM solutions address both: fine-tuning teaches a model your patterns, and private deployment keeps data inside your own environment.
Fine-tuning is not always the answer. We first test whether better prompts or retrieval-augmented generation solve the problem, because they are faster and cheaper to maintain. When fine-tuning is the right choice, we prepare high-quality training examples, train efficiently and prove the improvement with benchmarks against the base model.
- 01Confirm the need
- 02Select a base model
- 03Prepare training data
At a glance
Custom LLM Solutions & Fine-Tuning
Best for
- Domain-specific assistants for legal, medical or technical language
- Consistent structured output for data extraction
- Brand-voice content generation
- Private models for regulated or confidential data
What you gain
- Domain-accurate output
- Data stays private
- Lower running costs
Built with
- Python
- PyTorch
- Hugging Face Transformers
- PEFT / LoRA
- Llama
- Mistral
What our Custom LLM Solutions & Fine-Tuning service includes
Model selection
Comparing commercial and open-weight models for accuracy, latency, cost and licensing.
Training data preparation
Curating, cleaning and formatting instruction and example datasets from your data.
Fine-tuning
Supervised fine-tuning and parameter-efficient methods such as LoRA and QLoRA.
Private deployment
Self-hosting models in your cloud account or on-premises so data never leaves your control.
Evaluation benchmarks
Task-specific test sets to compare fine-tuned models against baselines.
Inference optimisation
Quantisation, batching and caching to reduce latency and running costs.
Benefits of Custom LLM Solutions & Fine-Tuning
What your business gains when it is designed, built and run well.
Domain-accurate output
Models use your vocabulary and follow your formats more reliably.
Data stays private
Self-hosted models keep prompts and data inside your infrastructure.
Lower running costs
A smaller fine-tuned model can replace a larger general model for specific tasks.
Predictable behaviour
Consistent output structure makes downstream automation more reliable.
Our method
How we build Custom LLM Solutions & Fine-Tuning solutions
A practical, step-by-step approach that proves value early and reduces risk.
Step 1
Confirm the need
We compare prompting, RAG and fine-tuning on your task to choose the most cost-effective option.
Step 2
Select a base model
We shortlist commercial and open-weight models by quality, size, licence and hosting needs.
Step 3
Prepare training data
We curate and clean examples from your data and format them for training.
Step 4
Fine-tune and benchmark
We train with efficient methods such as LoRA and measure results against the base model.
Step 5
Deploy and serve
We deploy with optimised inference, monitoring and a plan for future retraining.
Common use cases
- Domain-specific assistants for legal, medical or technical language
- Consistent structured output for data extraction
- Brand-voice content generation
- Private models for regulated or confidential data
- Smaller, faster models to reduce API costs at scale
Technologies we work with
- Python
- PyTorch
- Hugging Face Transformers
- PEFT / LoRA
- Llama
- Mistral
- vLLM
- Ollama
- OpenAI fine-tuning API
- AWS / Azure / GCP GPUs
Where Custom LLM Solutions & Fine-Tuning helps
Examples of how teams in different industries apply it.
Legal and compliance
Models that understand legal language and produce structured summaries.
Healthcare and life sciences
Private models for sensitive, domain-specific text.
Financial services
Consistent extraction and classification on confidential data.
Software and SaaS
Smaller, faster models embedded in products at scale.
Glossary
Custom LLM Solutions & Fine-Tuning key terms
Plain-English definitions of the terms used on this page, so everyone on your team is speaking the same language.
Read the FAQ- Fine-tuning
- Further training a pre-trained model on task-specific examples to change its behaviour.
- LoRA
- Low-Rank Adaptation, an efficient fine-tuning method that trains a small number of extra parameters.
- Open-weight model
- A model whose trained weights are published so it can be self-hosted, subject to its licence.
- Quantisation
- Reducing the numerical precision of a model to make it smaller and faster to run.
Our process
How we deliver
Design first, build carefully, test everything. Every AI, web and app project moves through the same six stages, adapted to your requirements.
- Mockups approved before any code is written
- Tested across screen sizes and browsers
- Direct communication throughout
- Support after launch
Step 01
Discover and plan
We review your goals, requirements and existing systems, agree the scope and assemble the right team.
- Requirements review
- Agreed scope
- Project team
Step 02
Design mockups
Our graphic designer creates mockups first. Development starts only after you approve the design.
- Mockups
- Your approval
- Figma or XD files welcome
Step 03
Develop
We build to closely match the approved design, responsive on desktop, tablet and mobile, with content that is easy to edit.
- Responsive build
- Editable content
- Progress updates
Step 04
Test
We test spacing, typography, interactions and functionality against the mockup, across screen sizes and browsers.
- Design accuracy
- Cross-browser
- Mobile and tablet
Step 05
Launch
We deploy to production, make final refinements and hand over everything you need.
- Production deployment
- Final refinements
- Handover
Step 06
Support
We stay available for maintenance, updates, performance improvements and the next release.
- Maintenance
- Updates
- Performance
FAQ
Custom LLM Solutions & Fine-Tuning: frequently asked questions
Common questions about our Custom LLM Solutions & Fine-Tuning service.
When should we fine-tune an LLM instead of using prompts or RAG?
Fine-tune when you need a consistent style, format or specialised task behaviour that prompting cannot achieve reliably, or when a smaller fine-tuned model can replace a larger, costlier one. For up-to-date facts, RAG is usually the better choice.
Can we run a language model on our own servers?
Yes. Open-weight models can be deployed in your own cloud account or data centre, which keeps prompts and data inside your environment.
How much training data is needed for fine-tuning?
Parameter-efficient fine-tuning can show results with a few hundred to a few thousand high-quality examples. Quality and consistency of examples matter more than volume.
Who owns the fine-tuned model?
Ownership terms are agreed in your contract. Model licences also apply, so we confirm the licence of the base model before training.
What hardware is needed to self-host an LLM?
It depends on model size and traffic. Smaller models can run on a single GPU server, while larger models need more GPU memory. We size infrastructure in your cloud account or data centre during planning.
How often does a fine-tuned model need retraining?
Only when your task, terminology or required output changes significantly, or when a better base model becomes available. We monitor quality so you know when retraining is worthwhile.
Related services
AI Knowledge Assistants (RAG)
Retrieval-augmented assistants that answer questions from your documents with cited sources.
Generative AI
Text, image and document generation built into your products and internal tools.
AI Integration & Deployment
Production-grade integration of AI into your software, cloud and security stack.