Use Case
AI Grounded in Your Knowledge
Retrieval-augmented generation connects language models to your live data sources, so every answer is accurate, cited, and drawn from information you actually own and control.
Language models have a fundamental limitation: their knowledge freezes at the point of training. RAG solves this by dynamically retrieving relevant content from your own data: internal wikis, documentation, databases, contracts, support tickets, product catalogs, and injecting it directly into the model's context at inference time. The model answers from real, current information rather than relying on what it memorized months ago.
The architecture pairs a retrieval layer (typically a vector database and embedding model) with a generation layer (the LLM itself). When a query arrives, the system finds the most semantically relevant chunks of your content, prepends them as context, and lets the model synthesize a coherent, grounded response. The result is far fewer hallucinations and answers that can be traced back to a specific source, critical for regulated industries and high-stakes decisions.
ProvenAI builds production-grade RAG pipelines end to end: data ingestion and chunking strategies, embedding model selection, vector store setup and optimization, hybrid search (dense + sparse), reranking, and prompt engineering for faithful summarization. We also instrument relevance and faithfulness metrics so you can measure quality and iterate with confidence.
Common applications include internal knowledge assistants, customer-facing support bots, document Q&A tools, and compliance research systems: any scenario where the model must reason over a large, evolving body of proprietary content without baking that content into model weights.