Generative AI on AWS, built for production and run by one accountable team.

AXIOMIO builds and runs generative AI on AWS: retrieval-augmented generation (RAG) knowledge assistants, AI agents that act inside your systems within approved permissions, and managed AI operations covering monitoring, evaluations, guardrails, security and token cost. AXIOMIO is an AWS Advanced Tier Partner, AWS Managed Service Provider and Level 1 MSSP, and has delivered production AI features for SharpSell and CricClubs.

Frequently asked questions

What AI services does AXIOMIO offer?

Three, which can be taken together or separately: RAG development (knowledge assistants that answer from your documents with citations), agentic AI (agents that carry out tasks in your systems within approved permissions), and managed AI operations (24/7 monitoring, evaluations, guardrails, security and cost control for AI in production).

Why use a managed service provider for AI instead of an AI consultancy?

Because most of the risk in AI arrives after launch: quality drift, model changes, new attacks and growing token bills. An AWS MSP and MSSP already runs production systems 24/7 with on-call engineers, a SOC and FinOps. We build with that in mind and keep running what we build, so nothing falls between a build team and an operations team.

Which models and AWS services do you use?

We are model-agnostic. Most work runs on Amazon Bedrock, which offers models from Anthropic, Amazon, Meta, Mistral and others, with Bedrock Knowledge Bases, Guardrails and AgentCore. Where a project needs it we use the model provider's API directly, as we did for SharpSell and CricClubs. We choose models on evaluation results and cost per request.

Where does our data go?

The application, index, logs and agents run in your AWS account and Region. Amazon Bedrock does not use your prompts and outputs to train models or share them with model providers. We document every data flow before launch for your security review.

How do we start?

With a conversation about one use case and the outcome you want. Discovery then checks data readiness and agrees success measures, and you get a fixed scope and quote for a pilot that runs on your own data, with evaluations and a cost estimate per request.