AI in Production
Put AI to work on your business outcomes, with cost and security in check.
We help you decide where AI is worth doing, then get it into production on AWS with the controls your board, your auditor and your regulator will ask about.
Sound familiar?
- “We want to use AI, but we can’t tell what it will cost or what it will do with our data.”
- “Our pilot works. Nobody will sign off on putting it in front of customers.”
- “People are already pasting client information into AI tools.”
- “The board wants an AI strategy and we don’t know where to start.”
What good looks like
- A short list of use cases worth doing ranked by value, feasibility and risk.
- A register of automated decisions with an owner for each, ready for the December disclosure.
- Guardrails built in every decision logged, people reviewing what matters, data kept in your own AWS account and region.
- Cost you can predict at production volume, before you commit.
- One use case in production then the next, on the same foundations.
How we do it
Assess
Where AI pays for your business, whether your data can support it, and what your environment needs before anything goes live.
Prove
We uplift the environment first, then run a pilot with logging, human review and cost limits in place from day one.
Scale
One application into production with monitoring for answer quality and drift, then the next use case on the same platform.
The 10 December 2026 deadline
From 10 December 2026, organisations covered by the Privacy Act must explain in their privacy policy when they use personal information in automated decisions that could significantly affect people. Most mid-market teams do not yet have a list of where those decisions happen. Our AI Business Assessment produces that list as one of its outputs. This is not legal advice; your lawyer still signs off the policy wording.
Decide what is worth doing
AI Business Assessment
A fixed-price assessment of where AI creates value for your business and whether you are ready for it. Built on the AWS AI Business Strategist framework and led by a certified AI Business Strategist.
What you get
- Use cases ranked by value, feasibility and risk
- Data readiness for each priority use case
- A first register of automated decisions and their owners
- Governance, risk and people readiness
- A 90-day roadmap with one recommended pilot
Turn it into a working system
AI Solution Build
We take a priority use case, from the assessment or one you already have, and build it as an AI application or agentic workflow on AWS, with your team alongside.
What you get
- A working application or agentic workflow on Amazon Bedrock
- Connected to your data and the systems it needs to act on
- Every decision logged, with people reviewing the steps that matter
- Cost limits and monitoring in place from day one
- Handover so your team can run and extend it
Make it safe to run
AI Production Readiness Review
A fixed-price review of an AI workload before, or soon after, it goes live.
What you get
- Security and data controls
- Cost model at production volume
- Logging, human review and escalation
- Monitoring for answer quality and drift
- The gaps to close before go-live, in priority order
“Nuvrix delivered a working AI agent for our GP placement process in a week. They designed it so every decision the system makes is logged and nothing goes to an agency without one of our own people reviewing it first. It’s given us a clear, low-risk view of what’s possible.”
Common questions
Do we need to be on AWS already?
No. Part of the work is often getting your data somewhere AI can use it safely. If that involves a migration, AWS funding programs can apply to AI workloads too.
Does the December disclosure obligation apply to us?
It applies to organisations covered by the Privacy Act, which generally means annual turnover above $3 million, plus some smaller organisations such as health service providers. We help you work out where automated decisions happen in your business; your lawyer confirms the obligation and the policy wording.
Will our data be used to train AI models?
Not with the approach we use. We build on Amazon Bedrock, where your prompts and data stay in your AWS account and are not used to train the underlying models.
Which models do you work with?
Whichever fits the job and the budget, from those available through Amazon Bedrock, including Anthropic’s Claude and Amazon’s own models. We test a few against your real task before recommending one.
Can we audit what the AI decided?
Yes. We design for it from the start: every decision is logged with its inputs and outputs, and the steps that matter go to a person before anything leaves the business.
Related solutions
Find out in 30 minutes whether we can help, and how.
Bring the problem, and we’ll tell you what we’d do first.