AurosIQ – AI Architecture for Engineering and Manufacturing

Add Context and Dynamic Execution

AurosIQ is powered by content that is enriched by context and encoded for execution.

• Context is defined by governing conditions, affected parameters, constraints, and rules which are codified into structured logic models – that can be reused, evaluated, and evolved across projects and programs. Traditional content which is based on data and text captures what, when, who, and which. Context introduces how and why.

• Execution is made possible by the fact that cognition models are encoded as structured, executable logic blocks. This enables Agentic AI automation to replace manual processes. Decisions can be evaluated in real time. Reviews become continuous and executed against live connected logic. Lessons start to function as active constraints within ongoing work rather than passive records of past events.

Target High Impact Processes for AI Automation

AurosIQ customers have keyed in on three well-defined targets for implementing AurosIQ, with the goal of realizing immediate and measurable results.

• Model-Based Requirements & Specifications – AurosIQ extracts, organizes, and aligns requirements from OEM-provided PDF documents and Excel files with Agentic AI automation. Conflicts and variations are highlighted early. Changes are automatically synchronized as specs evolve – maintaining coherence as requirements and specifications move downstream into engineering and manufacturing workflows. This leads to continuous compliance and built-in traceability that scales across multiple OEMs, programs, and supplier tiers.

• Model-Based Continuous Review – AurosIQ replaces milestone driven phase-gates with continuous evaluation from inception through every stage of the project. Issues are detected early and upstream, leading to significant reductions in risk and cost. Evidence accumulates in-process and documentation related to review signoff is auto-generated. Decision logs and status indicators emerge organically from continuous execution creating an audit-trail that can be trusted.

• Model-Based Quality – AurosIQ evaluates quality as work progresses with dynamic execution of cognition models which encode quality goals, processes, and metrics. Smart Work Instructions that adapt to live plant floor conditions including operator inputs, equipment status, or part variations are automatically generated.

In manufacturing, AurosIQ finds wide use in planning, process validation, and production part approval – for supplier parts as well as internally manufactured parts. Quality Engineers drive gate deliverables, supplier quality, and production/assembly operations from a single source of modeled data. Where requirements and standards are defined upstream, all downstream actions are automatically validated against authoritative specifications.