RAG Development Services

RAG (Retrieval-Augmented Generation) connects LLMs to your actual data, so they answer based on facts rather than training data. Instead of an LLM guessing or hallucinating, it retrieves relevant documents from your knowledge base and generates answers grounded in that information. This reduces hallucinations by 70-90% and provides source citations so users can verify every answer.

From $5K for a proof of concept to $75K for enterprise agentic RAG. A production single-source system typically costs $15K-$25K. Enterprise multi-source systems with compliance requirements run $30K-$45K. We recommend starting with a $5K-$10K POC to validate accuracy on your actual data before committing to a full production build.

1-2 weeks for a POC with a single data source. 3-6 weeks for a production system with hybrid search and monitoring. 6-12 weeks for enterprise multi-source systems with RBAC, compliance, and advanced evaluation. 8-16 weeks for agentic RAG with autonomous reasoning capabilities.

Virtually any data source - PDFs, Word documents, wikis, databases, APIs, Confluence, SharePoint, Google Drive, Slack archives, email, Notion, and more. We handle both structured data (databases, spreadsheets) and unstructured data (documents, images with OCR, audio transcripts). The key is choosing the right chunking and indexing strategy for each data type.

Encryption at rest and in transit, role-based access control (RBAC), comprehensive audit logs, and optional on-premise deployment where your data never leaves your infrastructure. We build HIPAA-compliant and SOC 2-compliant systems. Vector databases are configured with tenant isolation, and we implement document-level access controls so users only retrieve what they're authorized to see.

RAG is the right choice when your data changes frequently, you need source citations, or you want answers grounded in specific documents. Fine-tuning is better for teaching an LLM domain-specific language, tone, or behavior patterns. Most enterprises need RAG first - it's faster to implement, easier to update, and provides verifiable answers. We add fine-tuning when needed for specialized vocabulary or output formatting.

We use automated evaluation frameworks including Ragas and custom benchmarks tailored to your use case. Key metrics include faithfulness (does the answer match the source?), relevance (did we retrieve the right documents?), answer correctness, and latency. In production, we run continuous evaluation with quality alerts so accuracy is monitored 24/7, not just at launch.

Yes. 100% code and IP ownership. You own the entire RAG pipeline - ingestion, indexing, retrieval, generation, and monitoring code. You can deploy on your own infrastructure, modify as needed, and maintain full control of your data and systems. No vendor lock-in, no licensing fees on code we build for you.