Custom LLM model training and deployment for domain-specific AI applications. Fine-tune foundation models for your unique business requirements.
Production-ready solutions with proven results
Train models that understand your specific industry, terminology, and workflows.
Teach the model your preferred tone, format, and response style for consistent outputs.
We help you curate and format high-quality training data for optimal results.
Choose the right base model (GPT-4, Claude, Llama) based on your needs and budget.
Rigorous testing and validation to ensure model quality before deployment.
Your training data is handled securely with enterprise-grade privacy.
From concept to production in 8-12 weeks
Define the specific behavior, tone, and tasks you want the model to excel at. Identify training data sources.
Curate high-quality training examples. Format data properly. Create validation sets for testing.
Fine-tune the selected base model. Experiment with hyperparameters. Run multiple training iterations.
Rigorous testing against validation sets. Deploy to your infrastructure. Monitor performance.
Choose the right approach for your specific needs
| Feature | RAG & GraphRAG | LLM Fine-tuning This Page | AI Agents |
|---|---|---|---|
| Best For | Dynamic knowledge, Q&A | Domain-specific tasks | Complex workflows |
| Setup Time | 2-4 weeks | 4-8 weeks | 3-6 weeks |
| Cost | $$ | $$$ | $$ |
| Accuracy | High with good data | Very high | Variable |
| Maintenance | Low | Medium | High |
| Use When | Need latest information | Need consistent behavior | Need autonomy |
See how we've helped businesses achieve their goals
Generic LLMs struggled with financial jargon and regulatory requirements, leading to 40% accuracy on document classification tasks.
Learn more from our expert insights and implementation guides.
Master prompt engineering with proven techniques, real-world examples, and practical strategies for getting the best results from large language models.
Learn how to implement Retrieval Augmented Generation (RAG) systems that power intelligent applications with your own data.
A comprehensive guide to fine-tuning large language models for your specific use case, including when to fine-tune vs. using RAG.
Learn how to build autonomous AI agents that can reason, plan, and take actions to accomplish complex tasks in production environments.
Fine-tuning is the process of taking a pre-trained language model and training it further on your specific data to specialize its behavior. This teaches the model your domain expertise, preferred tone, and custom workflows.
Typically 50-500 high-quality examples for basic fine-tuning, and 1000+ examples for complex behavior changes. Quality matters more than quantity. We help you determine the right amount based on your goals.
Total project timeline is typically 6-12 weeks: 1-2 weeks for data preparation, 2-4 weeks for training iterations, and 1-2 weeks for evaluation and deployment.
Costs vary based on the base model, training data volume, and number of iterations. We provide detailed cost estimates during the discovery phase. Typical projects range from $20k-$100k.
Still have questions? We're here to help. Contact us for more information.
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