Understand how the engine is designed, how we approach a project and where immersive experiences fit.
Frequently asked questions
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From context to useful work.
01What is the Connected Labs AI engine?+
It is an orchestration engine designed to connect organizational knowledge, AI models and tools around a task. The goal is a result that people can review and reuse with its context. The engine page illustrates this through an employee onboarding guide.
Orchestration coordinates an AI task: preparing relevant context, selecting models and tools, applying access boundaries and defining human approval points. It is the organizing layer that connects a request to a workflow and an accountable person.
03Why does organisational knowledge matter?+
A general model does not automatically understand your procedures or working context. Our approach starts with data governance: identify trusted sources, assess their quality, define access and determine how information stays current. Those requirements would be defined with each organization.
04Where do people stay involved?+
Human oversight means assigning responsibility for the task, reviewing evidence and deciding which actions need approval. AI can produce incomplete or incorrect results, so our approach includes review and escalation appropriate to the use case and its risks.
05What does AI sovereignty mean in practice?+
For our engine project, sovereignty means designing for organizational control over strategic data, intellectual property and technology choices. This includes where data may be stored and processed, who can use it and which providers can be involved. Hosting, residency and access requirements must be agreed and validated for each deployment.
06How do visibility and traceability support governance?+
Visibility identifies the sources, models, tools and permissions involved in a workflow. Traceability connects inputs, outputs and human review decisions so teams can examine what happened. The controls and evidence required for governance or compliance assessments are defined around the organization’s requirements.
07How would you measure the value of AI adoption?+
Begin with a baseline for a specific workflow: time spent, output quality, cost and the people involved. Agree on success criteria, evaluate representative tasks and document limitations. This evidence should guide the decision to refine, expand or stop a use case.
We start with the task, the people involved and the information they use. The conversation helps identify the scope to investigate, access and governance requirements, and the outcomes to evaluate. We agree on a next step based on those needs.
09Can we work together on an immersive experience?+
Yes. Our immersive practice focuses on language learning, professional development, entrepreneurship and orientation. Tell us about the audience, the situation they need to practise and the interactions that would make the experience useful.