RAG Implementation

For well-structured knowledge bases with clear questions, we typically achieve 90-95% accuracy with proper citation. The key factors are document quality, chunking strategy, and retrieval tuning. We always build an evaluation suite so you can measure accuracy objectively and track it over time. For ambiguous or poorly documented domains, accuracy starts lower but improves as we refine the pipeline and add more source material.

We build incremental ingestion pipelines that detect changes in your source systems (Confluence edits, new SharePoint uploads, Git commits) and automatically re-process only the affected documents. Most systems update within minutes of a source change. We also handle version tracking so the system knows which version of a document it is referencing in its answers.

RAG can be deployed entirely within your cloud account — your documents never leave your infrastructure. We support private embedding models for sensitive data, role-based access controls so users only retrieve documents they are authorized to see, and audit logging for every query. For regulated industries, we have deployed HIPAA-compliant and SOC 2-aligned RAG systems.

ChatGPT file upload works for small-scale, ad-hoc queries. Production RAG handles thousands of documents, supports concurrent users, provides citation verification, integrates with your existing systems, updates automatically, and scales without hitting token limits. It is the difference between a prototype and a production system that your team relies on daily.

It depends on your requirements. Pinecone if you want zero infrastructure management. Weaviate for hybrid search with filtering. Qdrant for high-performance self-hosted deployments. pgvector if you already use PostgreSQL and want to minimize new infrastructure. We benchmark all viable options against your data and query patterns during the engagement — you do not need to decide upfront.