Serverless RAG on AWS Lambda
A privacy-first, infinitely scalable RAG pipeline we built with AWS Lambda and local LLMs.
View projectWe build production Retrieval-Augmented Generation systems that turn your private documents, knowledge bases, and databases into an accurate, citeable AI search experience — no hallucinations, full source traceability.
We ingest your PDFs, wikis, tickets, contracts, and databases, then build a semantic index so your team — or your customers — can ask questions in plain language and get grounded answers with citations back to the source.
For regulated industries, we deploy RAG entirely within your infrastructure using local embedding models and self-hosted LLMs. Sensitive data never leaves your VPC, keeping you compliant while still getting best-in-class retrieval quality.
Beyond basic retrieval, we build agentic RAG systems with query planning, multi-step reasoning, and self-correction — so the system decomposes hard questions, retrieves iteratively, and verifies its own answers before responding.
Battle-tested orchestration for document loading, chunking, retrieval, and prompt templating.
Pinecone, Weaviate, pgvector, and Qdrant — chosen and tuned for your scale and latency needs.
From self-hosted Llama and Mistral to Claude and GPT — matched to your privacy and quality bar.
Scalable AWS/GCP deployments, including serverless ingestion pipelines that eliminate idle cost.
Retrieval-Augmented Generation retrieves relevant info from your own data and feeds it to an LLM, so answers come from your documents instead of guesses — with far fewer hallucinations and full source citations.
A chatbot answers from fixed pre-trained knowledge. A RAG system searches your live private data at query time, so answers stay accurate, current, and traceable to source documents.
Yes — we build privacy-first pipelines with local embeddings and self-hosted LLMs so sensitive data never leaves your VPC or on-premise environment.
A proof of concept takes 2–4 weeks; a production pipeline with hybrid search, re-ranking, and monitoring typically takes 6–12 weeks depending on scope.
Let's turn your private data into an accurate, citeable AI assistant. Book a technical scoping call with our RAG architects.
Talk to Our RAG Architects