01Start with the work, not the model
Our approach is to define the business task, the people affected, and what a good result looks like before selecting AI. Some tasks are better served by a straightforward rule, a clearer process, or no automation at all.
The principles below guide how we scope work. A project agreement should specify the actual controls, deliverables, acceptance criteria, and ongoing responsibilities. This page is not a certification or a claim that every system is suitable for every use.
02Make authority explicit
A draft, recommendation, and authorized action are different things. We work to distinguish them in the workflow: what the AI can prepare, what it can do automatically, and what needs a person’s approval.
Decisions affecting safety, employment, access to essential services, money, or legal rights call for additional scrutiny and appropriate professional oversight. AI output alone should not be treated as sufficient authority for a consequential decision.
03Agree how data may be used
Before connecting business information, define the necessary data, who may access it, which providers process it, and how it will be retained or removed. Model-training use, data location, and provider settings need explicit consideration rather than assumptions.
Start evaluation with synthetic, redacted, or limited examples where practical. Access to a folder or platform should not automatically give an AI system permission to expose everything in it or take action across the business.
04Test the difficult cases
Useful evaluation includes incomplete notes, conflicting documents, unusual requests, and cases where the correct answer is “I don’t know.” For systems that retrieve documents or use connected tools, consider malicious instructions, inappropriate disclosure, and unauthorized actions as well as ordinary accuracy.
Where outputs may affect people differently, assess relevant groups, accessibility needs, and potential bias. A successful demonstration is a starting point for evaluation, not proof that a workflow is ready for unrestricted use.
05Show what happened and what needs checking
People should be able to understand when AI is involved and the limits of its output. Where appropriate, provide the source material, show missing information, and make approval or correction straightforward. A confident answer or a confidence score does not prove correctness.
Our website uses illustrative imagery, including AI-generated images, and prepared workflow animations. These explain possible services; they do not depict verified client results, actual staff, or a live AI processing your information.
06Plan for correction and change
Before launch, agree who owns the workflow, what to monitor, what counts as an incident, and how to stop or bypass automation. Rollout can begin with drafts or a limited pilot before wider permissions are considered.
Models, source information, and business processes change. Monitoring, re-evaluation, maintenance, and incident support need named owners and an agreed scope; they should not be assumed to run indefinitely after a project ends.
07Boundaries and concerns
We do not support AI projects intended for fraud, impersonation, unlawful discrimination, unauthorized surveillance, or circumventing safety and access controls. A request may need redesign, specialist review, or a decision not to proceed.
To raise an AI concern, email contact@airinteractive.io with the workflow involved, what happened, and the potential impact. Do not include sensitive records in the initial message. If a system may cause harm, use the agreed human fallback or pause process while the issue is assessed.
For a public reference on evaluating AI risk, see the NIST AI Risk Management Framework. Referencing it does not imply certification or endorsement.