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CodeNinjatools
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engine-mro-shop-us-ontology

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physical-ai
sovereign-ai
united-states
aviation-and-aerospace-manufacturing
industrial-ai-reference-architecture
ontologies
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Reasoning model
GLM 5.3 open weights, 753 billion parameters filed, served at INT4 (376 GB of weights) on vLLM on one node in the shop's own server room, GLM-5.3 bespoke licence
The register's one held model, frontier class at or above the 300 billion parameter threshold; carries the agentic work surface (critical path, workscope and vendor drafting agents, the command center and the cited conversational interface) and ontology maintenance; the licence permits commercial use, fine tuning and r...
Model set aside
gpt-oss-120b, US origin, permissive and ungated terms, proposed for a site inference server
Its source was never returned by the run's search, so it stays outside the register as open work; the requirement named it and the site inference server's node count is open until the register's held model is sized (chapter 9.5)
Retrieval model set aside
BGE-M3, proposed for citation retrieval
Set aside with provenance unrechecked, so the register carries no second model; the citation path is open work
Frontier hardware
80 GB HBM GPU class, eight GPUs per node, one node (640 GB)
Holds the 376 GB of INT4 weights with 264 GB beside them for KV cache and activations against the 1.2 planning factor; headroom lets the KV cache ceiling rise with concurrent planner sessions
Frontier footprint sizing rule
Frontier Model GPU Footprint Sizing Rule (vLLM, SGLang)
Converts filed parameter counts and precision into a node count before any purchase: 376 GB of weights times 1.2 is 452 GB against the node's 640 GB
Serving runtime
vLLM
The stack's on site language model serving choice, serving the INT4 weights within the KV cache ceiling the sizing reserves
Event backbone
Apache Kafka 4.4
Carries the streams between adapters and the object model; ordering from change data capture offsets and monotonic sequence numbers per partition, so no precision time protocol is needed; at least once delivery with idempotent consumers
Registry
Harbor, air gapped artifact and model registry
Artifacts and weights arrive signed inside the boundary; if the update path fails the running versions continue on pinned versions
Observability
Prometheus 3.13 LTS with Grafana and Loki
Carries the status lag gauge that shows how far the object model trails the systems of record, which outranks any serving benchmark
Time series store
InfluxDB 3 Core or TimescaleDB, chosen at design
Test cell data volumes are unstated, so the class is committed and the product is an open decision (chapter 9.5)
Sensing
None; records and plans, not cameras
Parts flow arrives as events from the systems of record, so there is no edge tier, no edge orchestration and no vision stack to license or maintain
Pattern: memory
Memory loops over confirmed delay causes and accepted recoveries, nothing trained into weights
The requirement asks that every confirmed outcome improve the next plan without training a model; the shop can read, audit and export the memory
Pattern: validation
Reconciliation of live statuses proved on a week of production data before any agent runs, then shadow mode beside the planners' process
An object model that cannot reconcile one part's status across the systems of record has nothing to draft from; agreement rates are logged and the disagreement path is built first
Pattern: export control fence
US person access control as an ontology property, with a data classification pass before go live
Engine technical data is export controlled and a non United States person's access is a deemed export under the ITAR; a network rule alone would break the join the ontology exists to build
Pattern: boundary
The shop's own servers, single site, segregated VLAN; customer, engine and vendor data never leave the shop's hardware; no third party model service; sovereign exit rehearsed in phase two
The operator's own words set the boundary, and the exit rehearsal proves the model, the memory and the object model leave together

Industrial AI Reference Architecture for an Engine MRO Shop Floor in the United States: ontology and model register

The object model and the model and equipment register from Industrial AI Reference Architecture for an Engine MRO Shop Floor in the United States, an open reference architecture by CodeNinja Atoms. Industrial AI reference architecture for an engine MRO shop in the United States: one shop ontology, drafting agents, GLM 5.3 on the shop's own node. Part of the Vertical-Driven Architectures series; every design in the series is also a row in the cumulative dataset https://huggingface.co/datasets/CodeNinjatools/vertical-driven-architectures.

Files

File What it holds
objects.json 15 typed objects (Engine, Customer, Workscope, Gate, Module, Part, Inspection finding, Disposition, Repair order, Vendor, Purchase order, Material lot, Build slot, Test, Release), with 16 typed links. Format hyper-ontology/1: designed with CodeNinja Praxis, implemented with Hyper Ontology.
models.csv The model and equipment register: every model and hardware class with its license and the reason for each choice (15 rows).

How to use it

Load objects.json with the Hyper Ontology loader (pip install hyper-ontology, see https://codeatoms.ai/developer/) or read it as plain JSON. The operator is described by class, never by name.

Made with CodeNinja Praxis and Hyper Ontology. One of the CodeNinja Atoms designs for industrial AI reference architecture. CC BY 4.0.
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