RAG development services on AWS

AXIOMIO builds retrieval-augmented generation (RAG) knowledge assistants on AWS. RAG answers questions from your own documents: it retrieves the relevant passages at question time and asks the model to answer only from them, with citations. We build the ingestion, permissions-aware retrieval, evaluation and guardrails on Amazon Bedrock in your AWS account, then run the assistant with the same 24/7 team that runs your cloud.

Frequently asked questions

What is retrieval-augmented generation (RAG)?

RAG is a way to make a language model answer from your own information. At question time the system retrieves the most relevant passages from your documents and gives them to the model, which answers from those passages and cites them. The model is not retrained, so new or changed documents are used as soon as they are re-indexed.

Should we use RAG or fine-tuning?

For answering questions from company knowledge, start with RAG: it keeps answers tied to current documents, shows sources and respects permissions. Fine-tuning changes how a model writes or behaves, not what it knows today. Some systems use both; we decide on evaluation results, not preference.

How do you stop the assistant making things up or exposing restricted documents?

Three ways. Retrieval only returns documents the user can open in the source system. The model is instructed and checked to answer only from retrieved passages, with contextual grounding checks in Bedrock Guardrails. And every release is scored against an evaluation set before it goes live, then sampled in production.

Is our data used to train the model?

Not on Amazon Bedrock: AWS states that it does not use your prompts and outputs to train models or share them with model providers. The assistant, index and logs run in your own AWS account, in the Regions you choose.

Which data sources can a RAG assistant connect to?

Common sources are Amazon S3, SharePoint and OneDrive, Confluence, file shares, wikis, ticketing systems and databases. We check access, format and freshness for each source during discovery, because that is where most of the effort goes.

How do you measure answer accuracy?

We build an evaluation set from real questions with expected answers and sources, then score retrieval (did we find the right passages), groundedness (is the answer supported by them) and answer quality. The same set gates every change to chunking, prompts or models.

How long does it take and what does it cost?

It depends mostly on how many sources there are, how clean they are and how complex the permissions are. After discovery you get a fixed scope and quote for the pilot, plus an estimate of running cost per answer at your expected volume.

Who runs the assistant after launch?

We can, as part of managed AI operations: monitoring, re-indexing, evaluations on every change, guardrail tuning, model upgrades and monthly quality and cost reporting. Or we hand it to your team with runbooks and code you own.