Retrieval-Augmented Generation architectures that ground AI responses in your proprietary knowledge — delivering accurate, cited, contextually intelligent answers at scale.
Large language models are powerful — but they hallucinate, have knowledge cutoffs, and know nothing about your proprietary data. Retrieval-Augmented Generation (RAG) solves this by connecting an LLM to a retrieval system backed by your own documents, databases, and knowledge sources. The model generates answers grounded in retrieved facts, with citations you can verify.
Dawnovation AI architects production-grade RAG systems handling millions of documents with sub-200ms response times. From document ingestion pipelines to advanced hybrid retrieval, re-ranking, and query routing, we build RAG systems that deliver enterprise-grade accuracy and reliability.
Ingest PDFs, Word docs, spreadsheets, web pages, Confluence, Notion, SharePoint, databases, and structured data sources — with automatic parsing and chunking.
State-of-the-art embedding models convert your documents into semantic vectors, stored in scalable vector databases (Pinecone, ChromaDB, Weaviate, pgvector).
Combine dense vector search (semantic similarity) with sparse BM25 keyword search for optimal recall across both conceptual and exact-match queries.
Retrieved chunks are re-ranked by relevance using cross-encoder models and compressed to fit optimal context windows, improving generation quality.
Automated pipelines keep the knowledge base current — detecting document changes, re-embedding updated content, and deprecating stale information.
Role-based document visibility ensures users only retrieve documents they're authorised to see — essential for enterprise deployments.
We catalogue your existing knowledge sources — documents, wikis, databases, emails, support tickets — and determine what belongs in the knowledge base.
Build automated pipelines to ingest, parse, chunk, and embed documents from all your sources on a scheduled or real-time basis.
Configure the vector database, define chunking strategies, tune embedding models, and implement hybrid retrieval with re-ranking.
Connect the retrieval layer to your chosen LLM, craft system prompts for grounded generation, and implement citation formatting.
Rigorous evaluation using RAG metrics (faithfulness, relevance, context recall) to benchmark and continuously improve answer quality.
Give employees instant, accurate answers from your entire policy library, product documentation, and historical records — without keyword search limitations.
Support agents (human or AI) instantly surface relevant knowledge base articles, past ticket resolutions, and product specs during live interactions.
Legal teams query contracts, regulations, and case precedents with natural language and receive cited, verifiable answers in seconds.
Analysts ask questions across earnings reports, SEC filings, and market research — with answers grounded in the actual source documents.
Core stack used for RAG Systems
Everything you need to know about our rag systems service.
Still have questions? →RAG constrains the LLM to generate answers based only on retrieved context. With proper prompt engineering, the model declines to answer when relevant context isn't found, rather than fabricating information. All answers can include source citations.
We support PDF, DOCX, XLSX, PPTX, HTML, Markdown, plain text, JSON, CSV, and can connect to Confluence, Notion, SharePoint, Google Drive, Slack, databases, and more via custom connectors.
Your knowledge base is completely private and isolated. Documents are never shared between clients or used to train external models. We support private cloud and on-premise deployments.
With a well-curated knowledge base and proper RAG configuration, clients typically achieve 90–97% answer accuracy on in-scope questions. We measure and report this rigorously during QA.
Ready to get started?
Book a free discovery call — we’ll scope your project and outline a clear path forward.
Book Free Consultation