Why this matters
Dataloop builds and deploys AI systems for enterprise clients, including government departments and organisations in regulated industries. That comes with responsibility, and we take it seriously.
This page is a plain English statement of how we approach AI in our work. It is not a legal document. It is a description of how we actually operate, written so that clients, partners, and procurement teams can see where we stand before they ever pick up the phone.
Our principles
Start with the problem, not the technology
We do not build AI for the sake of it. Every AI solution we deliver is built to solve a specific operational problem. If AI is not the right tool for the job, we say so, even when it means a smaller engagement.
Human oversight by design
We build AI systems with appropriate human-in-the-loop controls. Critical decisions are flagged for human review. Automation handles the repetitive work. People handle the judgement calls. We design the boundary between the two deliberately, not by accident.
Transparency with clients
We are upfront about what our AI systems can and cannot do. We do not oversell capabilities or hide limitations. Clients know how the system works, what data it uses, what assumptions it makes, and where its boundaries are.
Data privacy and security
Client data used in AI systems is handled in line with our privacy policy and any specific client agreement. We do not use one client’s data to train models for other clients. Data stays where it belongs.
Bias and fairness
AI systems can reflect biases in their training data or in the way they are designed. We are aware of this and work to minimise it. Where an AI system is making recommendations that affect people, we build in checks, review processes, and clear escalation paths.
Reliability over novelty
We build for production, not demos. Our AI systems are designed to run consistently and fail gracefully. If a system is uncertain, it says so rather than guessing. We would rather a system flag something for review than fabricate a confident answer.
How we build AI systems
For every AI engagement we follow the same approach:
- We assess the use case and decide together whether AI is the right fit.
- We define clear boundaries for what the system should and should not do.
- We build with monitoring and logging so performance can be tracked over time.
- We test against real-world data and edge cases before deployment.
- We train the client’s team on how the system works and how to manage it.
- We provide ongoing support and monitoring after deployment, not just a handover.
Our use of AI in our own work
We are practitioners, not just advisors. We use AI tools internally to support our own work, including for drafting, research, and code assistance. We use the same kinds of tools we recommend to clients.
Client deliverables are reviewed and quality-checked by our team, regardless of how they were produced. The standard we hold ourselves to is the same standard we hold the work to. AI helps us move faster. It does not replace our judgement.
Continuous improvement
AI is a fast-moving field. Responsible AI practice is too. We stay current with developments in the standards, guidance, and expectations that apply to AI delivery, particularly in Australian government and enterprise contexts.
We review and update our approach as those standards evolve. We welcome questions and feedback from clients on how we handle AI in their work.
Contact
Questions about our AI practices, or how we would approach a specific use case, can be sent to hello@dataloop.com.au.
Have a question about this?
We are happy to clarify anything on this page. Drop us a line.