Retrieval Augmented Generation Services

A focused RAG system — one document corpus, one user-facing interface, one LLM backend — typically reaches production in 10 to 14 weeks. The first two weeks are document audit and evaluation set construction. Weeks three through eight cover retrieval pipeline development, embedding selection, reranker integration, and iterative accuracy improvement against the benchmark. The final phase is UI integration, access-control wiring, and load testing. Multi-corpus systems with complex permission models or real-time ingestion requirements add four to eight weeks.

We've deployed RAG systems on pgvector (for teams that want everything in Postgres), Pinecone, Weaviate, Qdrant, and OpenSearch's k-NN plugin. LLM backends include OpenAI (GPT-4o, GPT-4o-mini), Anthropic (Claude 3.5 Sonnet and Haiku), Cohere Command R+, and self-hosted open models (Llama 3, Mistral) for clients with strict data-residency requirements. The stack is chosen based on your latency, cost, and privacy constraints — not because we have a preferred vendor.

We use a combination of prompt constraints (explicit instructions to respond only from provided context), confidence-score filtering (returning 'I don't know' when retrieval scores fall below a threshold), and automated faithfulness evaluation using an LLM-as-judge setup against the retrieved chunks. On sensitive use cases we also add a citation-grounding check that verifies each claim in the generated answer maps to a retrievable passage before the response reaches the user. This adds latency but is appropriate for compliance and clinical use cases.

Yes. For scanned documents or image-heavy PDFs we add an OCR preprocessing layer (AWS Textract, Azure Document Intelligence, or open-source alternatives depending on your environment), followed by layout-aware chunking that preserves table structure and heading hierarchy. For documents where visual layout carries meaning (financial statements, engineering drawings), we can incorporate multimodal embedding models that encode both text and visual regions. The tradeoff is higher ingestion cost and latency — we help you decide where it's worth it.

We build ingestion pipelines with document-level versioning in the vector store: when a source document is updated, all chunks derived from it are identified by document ID, deleted, and re-indexed from the new version. Deletions propagate the same way. For SharePoint or S3 sources, this is triggered by webhook events; for database sources, by CDC (change data capture) streams. The result is that retrieval always reflects your current document state, not a stale snapshot from the last full re-index.