In aerospace maintenance, the hard part isn’t analytics — it’s trust.

Nextspace turns scattered engineering and operational data into a single, contextual model you can interrogate and audit.

Here we start from the engineering truth: the CAD / PLM structure.

Not just geometry — the parts hierarchy, identifiers, and attributes that anchor everything else.

When we bring that model into Nextspace, it becomes the backbone of the ontology.

Every component is now a first-class entity that other systems can reference — consistently.

And because this is an operational platform, ingestion is automatable.

Imports can run as repeatable pipelines through the API, so your model stays current as designs and records evolve.

Next we add the real world: maintenance history, removals, health events, and schedules —

whether those arrive as files today or live feeds tomorrow.

Nextspace is built to amalgamate these sources into one contextual layer.

As each dataset lands, Nextspace captures its schema and meaning — not just columns.

That’s important, because AI and reporting only become reliable when the platform understands what data is, and how it relates.

Now we’re merging multiple sources into the same model.

This is where context compounds: logs, health data, and component events stop being separate tables —

they become connected facts around the same physical asset.

Nextspace then helps map relationships into the ontology.

You can connect by shared identifiers, by geometry, or with AI assistance —

and you can review and adjust the links, so it’s always explainable.

Once connected, those attributes are attached back to the 3D model.

So a part in the landing gear isn’t just a shape —

it’s a navigable “visual proxy” for everything known about that entity, across time.

Now we can ask higher-value questions in natural language.

The key difference is that answers are grounded in the full contextual ontology —

so you can see what was used, why it was used, and trace it back.

From that same query, Nextspace can generate interactive dashboards and reports —

faults over time, priority distributions, downtime drivers —

all produced from the same auditable model.

And with MCP integrations, AI agents can work through the full UI and ontology safely —

producing charts, summaries, and visual outputs while staying inside a governed, referenceable data context.

That’s the Nextspace advantage: contextualised data first —

then trusted AI analytics, reporting, and decision support you can actually defend.

If you’d like, we can map this to your fleet and your data sources.

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