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Custom LLM development
When off-the-shelf models don't fit your domain (your terminology, your output format, your compliance requirements), we build and fine-tune models specifically for your use case.
Pricing on request
Full process
- Use case validation & model selection
- Dataset curation & preparation
- Fine-tuning (LoRA, QLoRA, full fine-tune)
- Evaluation suite development
- RLHF / preference alignment (when needed)
- Deployment on AWS Bedrock, Azure, or GCP
- Monitoring & model versioning
When you need this
- Domain-specific terminology not in base models
- Consistent output format requirements
- Regulatory or compliance constraints
- Cost optimization (smaller deployed model)
- Proprietary knowledge that can't go to external APIs
- Latency requirements (on-premise deployment)
Not sure if you need fine-tuning?
Start with an AI audit. We'll tell you whether fine-tuning or RAG is the right approach for your use case.
Related
- When fine-tuning beats RAG
The cases where training on your data is the right call.
- RAG pipeline development
Often the cheaper first step before any fine-tuning.
- LLMs in secure environments
On-premise and air-gapped model deployment.
- Production model deployments
Fine-tuned models running in client environments.
Start with one workflow
Book a free 30-minute AI audit. We look at one workflow with you and tell you whether it can reach production in 6 to 8 weeks. No commitment, just 30 minutes with an engineer.
Book a free AI audit