Custom RAG Development

Retrieval-Augmented Generation (RAG) is an AI architecture that allows a language model to retrieve information from your company's documents, databases, or knowledge base before generating a response. Instead of relying solely on its training data, the AI uses your latest business information to provide more accurate, relevant, and trustworthy answers. RAG is ideal for internal knowledge assistants, customer support, technical documentation, and enterprise search and doesn’t require costly model training.

Project timelines vary based on the number of data sources, integrations, and security requirements. A proof of concept can often be delivered within a few weeks, while production-ready RAG systems with document processing pipelines, vector databases, user permissions, and enterprise integrations typically take one to three months. We begin every engagement with a discovery phase to define scope and implementation milestones.

We implement secure document ingestion, encrypted data storage, role-based access controls, secure API integrations, and deployment options ranging from cloud-hosted services to fully self-hosted infrastructure. For organizations with regulatory requirements, we can also build solutions that support standards such as HIPAA.

As your documentation, knowledge base, and business processes change, your RAG system should evolve with them. We offer ongoing maintenance that includes document pipeline improvements, retrieval optimization, embedding updates, performance monitoring, model upgrades, and infrastructure support to keep your AI delivering accurate and relevant responses.

We can build RAG systems using commercial AI providers or self-hosted open-weight models, depending on your security, compliance, performance, and cost requirements. We also support hybrid deployments that combine private infrastructure with external AI services when appropriate.

The cost depends on the complexity of your knowledge base, the number of data sources, user access requirements, integrations, and deployment model. A focused internal knowledge assistant requires a different investment than an enterprise RAG platform serving thousands of users across multiple systems. Following discovery, we'll provide a detailed proposal with transparent pricing, timelines, and recommended architecture.