Manufacturing operators often see early warning signs before enterprise systems
do: an unusual gearbox sound, a quality defect, a label changeover mismatch, a
work-instruction question, missing PPE, or a blocked walkway. These observations
are valuable, but they often stay trapped in verbal handoffs.
WearEdge Pro packages that frontline evidence into an OPEA-aligned
Manufacturing Agent Suite. The submitted competition artifact is not an
Android-only application. It is a Docker-runnable Web/API package with a
browser demo console, five agent routes, Qdrant-backed RAG, official OPEA TEI
embedding profile, guardrails, and evaluation evidence.
The five agent routes are:
Agent Workflow Target
maintenance Predictive maintenance from M400 evidence maintenance_work_order
iqc Incoming and in-process quality checks qms_quality_event
changeover SKU setup and first-piece verification changeover_checklist
wi Released work-instruction guidance wi_reference
hazard PPE, moving-parts, and walkway observations ehs_case
The architecture follows an OPEA-style path:
M400 / API evidence
-> Gateway
-> Manufacturing Megaservice
-> route registry
-> Dataprep
-> RAG / Retriever
-> Qdrant Vector DB
-> OPEA-compatible embedding service or official OPEA TEI profile
-> LLM adapter or deterministic demo path
-> deterministic evaluator
-> guardrails
-> bounded action card
The most important design decision is route isolation. Maintenance must not
issue safety clearance. Hazard observations must not invent final root cause.
Quality must not release a lot. Changeover must not grant restart permission.
Work-instruction guidance must stay tied to released source evidence.
For OPEA evidence, the repository includes:
Docker Compose base profile with Qdrant and the Manufacturing Gateway;
OPEA-compatible /v1/embeddings profile;
official OPEA TEI profile using Hugging Face TEI, opea/embedding:latest,
TEI_EMBEDDING_ENDPOINT, and OPEA_TEI_EMBEDDING;
OpenAI/OPEA-compatible LLM adapter boundary;
GenAIEval-compatible route evaluation package;
upstream OPEA RFC, comments, and a CI-green GenAIExamples PR.
The evaluation package includes 15 cases across the five routes and verifies:
action-card contract;
integration target correctness;
channel correctness;
risk-level correctness;
human gate correctness;
guardrail pass;
RAG source match;
route isolation.
The hardware evidence was captured on Google Cloud C3 c3-standard-4: a
single-node, 4-vCPU, 16-GiB-RAM, no-GPU Intel Xeon host exposing AVX-512 and AMX
flags. On that class of host, WearEdge validated the deterministic five-agent
route benchmark, Docker/Qdrant E2E, OPEA-compatible embedding profile E2E, and
official OPEA TEI profile E2E.
The public repository is here:
https://github.com/davidmillerak2026-sys/wearedge-opea-manufacturing
The upstream OPEA PR is here:
https://github.com/opea-project/GenAIExamples/pull/2462
WearEdge is still a prototype, not a certified safety or release controller.
The important point is the platform pattern: one OPEA-aligned manufacturing
suite can convert frontline evidence into bounded, auditable action cards
across maintenance, quality, changeover, work instructions, and safety.
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