Structured metamodel
Every artifact is typed and traceable. Requirements, components, tests, baselines, first-class entities with explicit relationships, not files in folders.
Glossary · Definition
An Engineering Operating System (Engineering OS, EOS) is how a hardware company transforms itself through operational excellence — and makes its engineering infrastructure a lasting industrial advantage. Instead of scattering requirements, components, BOMs, baselines, tests, and documents across PLM, ALM, and spreadsheets, it holds them as first-class, interconnected entities on one computable graph: the substrate a high-performing engineering organization runs on.
Where PLM systems manage parts and ALM tools manage work, an Engineering Operating System runs the entire product graph as live, queryable, traceable data — and turns that data into operational leverage. It is the operating layer that lets a hardware organization execute with the discipline, speed, and repeatability that operational excellence demands.
Modern hardware products are systems of systems, mechanical, electronic, firmware, AI. When their data lives in disconnected tools, every program pays an operational tax: rework, missed dependencies, audit scrambles. Generic project tools can't model these dependencies; legacy heavy-weight PLM was built for serial CAD-centric workflows. The Engineering Operating System closes that gap and turns the engineering infrastructure itself into an industrial advantage competitors can't copy overnight.
Every artifact is typed and traceable. Requirements, components, tests, baselines, first-class entities with explicit relationships, not files in folders.
Formulas, rollups, and impact analysis run on live data. Mass, cost, and coverage propagate up the BOM automatically the moment a leaf changes.
Design freezes are cryptographically enforceable baselines. Replay any past state. Lineage of revisions is formal, not folder-based.
Every change carries provenance. Audit trails are ready for ISO 13485, AS9100, DO-178C, exportable in one click, not assembled over weeks.
Humans, external tools, and AI agents access the same graph with the same scoped rights. MCP-native: agents operate inside your governance envelope.
Koddex provides the runtime for operational excellence: a typed data model, real-time co-editing, formal design reviews, baseline locks, impact analysis across the entire graph, and an MCP-native API that lets AI agents operate within the same governance envelope as your engineers. The payoff is an engineering infrastructure that compounds into an industrial advantage — faster programs, cleaner audits, fewer escapes.
Operational excellence is impossible on stale data. Every KPI, every compliance heatmap, every risk matrix is computed on the live graph. Open a dashboard, see today's state. No nightly batch jobs, no stale CSV exports.