Generative AI

RAG lets a model look up answers in your documents when a question is asked, while fine-tuning retrains a model on your examples to change how it writes or behaves. RAG suits content that changes often and makes answers easier to trace to sources. Fine-tuning suits a fixed style, format or domain task. Many projects combine both.

We reduce hallucinations by grounding answers in your own sources and checking outputs before users see them. In Medd AI, GPT-4o validates each draft for factual accuracy, AMA citation format and readability, and flags invalid drafts with issue logs. We also test against real examples and add guardrails for questions the data cannot answer.

We build data protection into each project from the start. API keys stay behind secure access layers, inputs and files are validated, and access is controlled with authentication and user roles. We also choose model providers and hosting to match your privacy and compliance needs.

We use the model that fits the task, including OpenAI GPT-4o, Google Gemini and Perplexity AI, orchestrated with LangChain and LangGraph. For GoAI Chat, we built a system that classifies each query and routes it to the best-fit model and agent using confidence scoring.

Cost depends on your project's scope rather than a fixed price. The main drivers are features, the model approach (API, RAG or fine-tuning), data preparation, integrations, compliance needs and ongoing model usage. A free 30-minute consultation gives you a scoped estimate for your project.

The timeline depends on scope and how ready your data is. A focused chatbot or content tool moves faster than a multi-step platform with several integrations, review workflows or fine-tuned models. In a free 30-minute consultation, we review your goals and give you a scoped estimate, including timeline.