Generative AI Development for Production

RAG (Retrieval-Augmented Generation) combines a language model with a search system that retrieves relevant information from your data before generating a response. This grounds the AI's answers in your actual content, dramatically reducing hallucinations and enabling the AI to provide accurate, up-to-date information specific to your business.

RAG is the right choice for most use cases because it works with your existing data and can be updated without retraining. Fine-tuning is better when you need to change the model's behavior, tone, or reasoning patterns, or when you need faster response times. Many production systems combine both approaches for optimal results.

We use multiple techniques: RAG to ground responses in verified data, confidence scoring to flag uncertain responses, output validation against known facts, and human-in-the-loop escalation for critical decisions. Our monitoring systems track hallucination rates in production and alert when quality degrades.

We design architectures that protect your data. Options include using enterprise API tiers that do not train on your data, deploying open-source models on your own infrastructure, pre-processing to remove PII before it reaches the LLM, and using Azure OpenAI or AWS Bedrock for data residency compliance.

A focused RAG-based chatbot or assistant typically costs $40,000-$80,000 to develop and deploy. Complex multi-agent systems or custom fine-tuned models range from $100,000-$250,000+. Ongoing API costs depend on usage volume but can be optimized significantly through caching and model selection strategies.

A production-ready RAG system typically takes 6-10 weeks from data preparation through deployment. Fine-tuning projects add 2-4 weeks for data curation and training. Complex multi-agent systems take 3-5 months. We deliver working prototypes within the first 2-3 weeks so you can validate the approach early.