AI in the engineering process. People accountable.
Years of practical AI adoption, applied to development, knowledge workflows and carefully bounded automation.
Start with a useful task
We apply AI where a workflow has a clear purpose and an output that can be checked. Examples include development assistance, working with technical documentation and connecting information across engineering systems. The model is one component in a workflow with defined inputs, tools and responsibilities.
Control data and tool access
Data sensitivity and the consequences of an action determine the controls. We consider what information a model needs, which systems it can access and where review is required. Credentials stay in the systems that own them, with access scoped to the task.
Validate consequential output
Generated code, summaries and findings require evidence. We use review and appropriate checks before accepting output or changing a running system. For security work, explicit authorisation and the agreed assessment scope remain the boundaries of the workflow.
Automate the repeatable parts
Useful automation makes a process more consistent and observable. Clear status, failure handling and a way to stop or recover matter as much as the successful path. We favour focused workflows whose result can be verified over open-ended autonomous access.