Custom LLM Development
We build private, GPT-style large language models trained or fine-tuned on your own data.
Avtrix AI Solutions builds private, custom large language models grounded in your own data — ChatGPT-style systems that never send your proprietary information to a third party, tuned specifically to your domain and use case.
What Is Custom LLM Development?
Custom LLM development means adapting a large language model — through fine-tuning, retrieval-augmented generation (RAG), or private hosting — so it understands your organisation's specific data, terminology, and workflows. Unlike calling a public API directly, a custom LLM deployment gives you control over data privacy, accuracy for your domain, and long-term cost as usage scales.
What We Deliver
Fine-Tuning Open-Source LLMs
Adapt models like Llama and Mistral to your domain vocabulary and tasks for higher accuracy than generic models.
Retrieval-Augmented Generation (RAG)
Ground the model in your live documents and databases so answers reflect current, accurate information.
Private & On-Premises Deployment
Host the model inside your own cloud environment or on-premises so your data never leaves your infrastructure.
Domain-Specific Tuning
Legal, medical, financial, or technical domain adaptation for terminology and reasoning specific to your industry.
Evaluation & Guardrails
Rigorous accuracy testing and safety guardrails before any custom LLM reaches production.
Ongoing Model Maintenance
Scheduled re-tuning and evaluation as your data and business needs evolve over time.
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Data Curation
We assess and prepare the documents, knowledge base, or historical data the model needs.
Approach Selection
We decide between fine-tuning, RAG, or a hybrid approach based on your accuracy and cost needs.
Build & Tune
We fine-tune or configure retrieval pipelines and evaluate against real queries from your domain.
Guardrail & Safety Testing
We test for hallucination, bias, and edge cases before granting production access.
Deployment & Monitoring
Deployed privately with usage monitoring and a plan for ongoing re-tuning.
Where Custom LLMs Pay Off
| Use Case | Business Outcome |
|---|---|
| Internal knowledge assistant | Employees get accurate, instant answers grounded in internal documents |
| Customer support copilot | Support agents resolve tickets faster with AI-suggested, on-brand responses |
| Domain research assistant (legal, medical, finance) | Faster research and drafting grounded in your own domain-specific corpus |
| Coding copilot for internal tools | Faster development on your proprietary codebase and internal APIs |
| Confidential document Q&A | Query sensitive documents without sending data to a public AI provider |
Public API vs. Private Custom LLM
Calling a Public API Directly
- Your prompts and data pass through a third party
- Generic knowledge, not tuned to your domain
- Per-token costs scale unpredictably with usage
- Limited control over model behaviour and updates
Avtrix Custom LLM
- Deployable privately so your data stays yours
- Tuned and grounded in your own domain and data
- Cost-optimised architecture for your usage pattern
- Full control over behaviour, updates, and guardrails
Custom LLM Development FAQs
Is a custom LLM more expensive than using ChatGPT or a similar API?
It depends on usage volume. At scale, a custom or self-hosted model is often cheaper long-term than per-token API costs, in addition to giving you data control.
Will our data ever leave our infrastructure?
No, when deployed privately or on-premises. This is one of the main reasons companies choose custom LLM development over public APIs for sensitive data.
How accurate is a custom LLM compared to GPT-4 or Claude?
For general knowledge, large public models are typically stronger. For your specific domain and documents, a tuned or RAG-grounded custom LLM is usually more accurate and reliable.
What hardware do we need to run our own LLM?
This depends on model size and usage. Many deployments run efficiently on modest cloud GPU instances; we size infrastructure to your actual needs, not the largest available option.
How often does the model need to be retrained or updated?
It depends on how fast your underlying data changes. RAG-based systems can be updated continuously; fine-tuned models are typically refreshed periodically.
How do you prevent the model from making things up?
We ground responses in your verified data through RAG, add citation of sources, and include evaluation and guardrail testing before launch.
Ready for a Private AI That Actually Knows Your Business?
Tell us what knowledge or workflow you want it grounded in, and we'll scope a plan.