Your Business,
Your Brain
Generic ChatGPT isn't enough. We fine-tune Custom LLMs on your proprietary data, creating an AI model that speaks your language and knows your secrets—securely.
Why Build a Custom LLM?
Data Security
Run open-source models (Llama 3, Mistral) on private servers.
Domain Expert
Models trained specifically on your legal, medical, or tech documents.
Cost Efficiency
Smaller, specialized models often outperform giant, expensive ones.
From Data to Deployed Model
Private AI models built on your data, running on your infrastructure.
Use Case Definition
Define what the model must do, what data it will train on, and how success is measured — accuracy, latency, and cost per query targets agreed upfront.
Data Preparation
Clean, structure, and format your training data. Build instruction datasets, preference pairs, or domain corpora depending on the fine-tuning approach.
Fine-Tuning & Evaluation
Train on Llama 3, Mistral, or other open-source base models. Evaluate against benchmarks and your specific test cases before any deployment.
Private Deployment
Deploy on your infrastructure using vLLM or Ollama. No data ever leaves your environment. Full documentation and handover included.
Custom LLM Use Cases
Specialised models that outperform general-purpose AI on your domain.
Document Intelligence
Extract, classify, and summarise from contracts, invoices, and reports — trained on your specific document formats.
Internal Knowledge Base
Answer employee questions from company docs, SOPs, and wikis. Accurate, grounded answers with source citations.
Customer Service AI
Trained on your product knowledge, policies, and support history. Knows your business — not just general web data.
Legal & Compliance
Trained on case law, regulations, and internal policies. Flag risks in contracts and draft clause suggestions automatically.
Medical & Clinical
GDPR-compliant clinical note summarisation and coding assistance, running fully on-premise with no external API calls.
Code Generation
Fine-tuned on your codebase conventions, internal libraries, and architecture patterns. Generates code that fits your standards.
Custom LLM Development FAQ
What's the difference between fine-tuning and RAG?
RAG retrieves relevant documents at query time and passes them to a general model. Fine-tuning changes the model's weights so it internalises domain knowledge. We typically recommend RAG for knowledge bases and fine-tuning for tone, format, and specialised reasoning tasks.
What base models do you work with?
Primarily Llama 3, Mistral, and Qwen — open-source models that can be deployed privately. We also work with Claude and GPT-4 for RAG pipelines where cloud hosting is acceptable.
How much data do I need for fine-tuning?
For instruction fine-tuning, 500–5,000 high-quality examples are typically sufficient. Quality matters more than quantity. We help you build the dataset if you don't have one ready.
How is the model deployed?
On your infrastructure using vLLM or Ollama depending on scale. We handle server setup, API endpoint configuration, authentication, and monitoring. You get full ownership and documentation.
Is this GDPR compliant?
Yes. All training and inference happens within your environment. No data is sent to third-party AI APIs during inference. We document data lineage and retention policies at scoping.
Ready to Build Your Private AI?
Book a free strategy call. We'll assess your use case, recommend the right approach, and scope an implementation that fits your data and budget.
Book a Free Strategy Call