Custom Language Model (LLM) Development for Your Domain

Fine-tuning adapts a pre-trained model with your domain-specific data, which is faster and requires less training data. Training from scratch builds an entirely new model, providing maximum customization but requiring substantially more data and computational resources. We recommend fine-tuning for most use cases, as it delivers superior results with efficient resource usage.

For fine-tuning, we typically need several hundred to a few thousand high-quality examples depending on your specific domain and task complexity. We work with you to collect, curate, and prepare your data. During our initial assessment, we evaluate your existing datasets and recommend the optimal data collection strategy to maximize model performance.

Yes, absolutely. We support full on-premise deployment of custom LLMs for organizations with strict data governance requirements. This includes air-gapped deployment, Docker containerization, and integration with your existing infrastructure. On-premise deployment ensures your proprietary data never leaves your network.

Data privacy is central to our approach. Your training data is never used to train other models, shared with third parties, or retained longer than necessary. We maintain comprehensive compliance certifications including HIPAA, SOC 2 Type II, and GDPR. We can also work within HIPAA BAAs, data processing agreements, and other compliance frameworks your organization requires.

We work with leading language models including GPT-4, Claude, Llama, Mistral, and other open-source alternatives. Our technology-agnostic approach means we select the best foundation model for your specific use case, budget, and deployment requirements. We evaluate trade-offs between accuracy, cost, speed, and compliance.

Typical projects range from 2-4 months from discovery through production deployment. Our approach starts with a focused discovery phase to understand your requirements, followed by iterative development with regular feedback cycles. We prioritize delivering value quickly through incremental deployments rather than extended development phases.

We implement comprehensive testing and validation frameworks including benchmark datasets, human evaluation, automated testing, and continuous monitoring. We measure performance against your business metrics, not generic benchmarks. Post-deployment, we track model performance and implement continuous improvement processes based on real-world usage patterns.