Your Business,
Your Brain
Generic ChatGPT is not enough. We train **Custom LLMs** on your proprietary data, creating an AI model that speaks your language and knows your secrets — securely.
domain-specific accuracy achieved
smaller models via fine-tuning vs general LLMs
average inference time
data privacy with on-premise deployment
Model Performance Benchmark
Fine-tuned models outperform general-purpose LLMs in domain-specific tasks
Accuracy
92%
Top domain fit
Latency
<200ms
Realtime responses
Model Size
-40%
Optimized footprint
Privacy
100%
On-prem deployment
GPT-4 General
Accuracy on domain-specific tasks
Latency
~480ms
Est. Monthly Cost
~€3,800
Privacy Control
Llama 3 Fine-tuned
Accuracy on domain-specific tasks
Latency
~190ms
Est. Monthly Cost
~€1,450
Privacy Control
Mistral Fine-tuned
Accuracy on domain-specific tasks
Latency
~160ms
Est. Monthly Cost
~€980
Privacy Control
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 technical documents.
Cost Efficiency
Smaller, specialized models often outperform large, expensive giants.
Deployment Options
Choose the infrastructure model that fits your security and budget requirements
On-Premise
Full control within your infrastructure
Advantages
- Complete data privacy
- No external API calls
- Custom hardware optimization
Considerations
- Higher upfront cost
- Requires IT maintenance
Private Cloud
Dedicated cloud environment
Advantages
- Scalable resources
- Managed infrastructure
- Quick deployment
Considerations
- Monthly operational cost
- Vendor dependency
Hybrid
Best of both worlds
Advantages
- Flexible scaling
- Sensitive data on-premise
- Cost optimized
Considerations
- Complex architecture
- Requires orchestration
Custom LLM Use Cases
Specialized models that outperform general-purpose AI in your domain.
Document Intelligence
Extracts, classifies, and summarises contracts, invoices, and reports — trained on your specific document formats.
Internal Knowledge Base
Answers employee questions about company documents, SOPs, and wikis. Precise, grounded answers with source citations.
AI Customer Support
Trained on your product knowledge, policies, and support history. Knows your business — not just generic web data.
Legal & Compliance
Trained on case law, regulations, and internal policies. Detects contract risks and automatically generates clause suggestions.
Medical & Clinical
Clinical note summarisation and coding assistance, GDPR-compliant, 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.
From Data to Deployed Model
Private AI models built with your data, running on your infrastructure.
Use Case Definition
We define what the model should do, what data it will train on, and how success is measured — accuracy, latency, and cost-per-query targets agreed from the start.
Data Preparation
We clean, structure, and format your training data. We build instruction sets, preference pairs, or domain corpora according to the fine-tuning approach.
Fine-Tuning & Evaluation
We train on Llama 3, Mistral, or other open-source base models. We evaluate against benchmarks and your specific test cases before any deployment.
Private Deployment
We deploy on your infrastructure using vLLM or Ollama. No data ever leaves your environment. Full documentation and handover included.
Custom LLM Development FAQ
What is 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 to internalise domain knowledge. We generally recommend RAG for knowledge bases and fine-tuning for tone, format, and specialised reasoning tasks.
Which base models do you work with?+
Mainly 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, usually 500–5,000 high-quality examples are 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 configuration, API endpoints, authentication, and monitoring. You get full ownership and documentation.
Is it GDPR compliant?+
Yes. All training and inference happen inside your environment. No data is sent to third-party AI APIs during inference. We document data lineage and retention policies during scoping.
Ready to Build Your Private AI?
Book a free strategy call. We'll evaluate your use case, recommend the right approach, and define an implementation that fits your data and budget.
Book a Free Strategy Call