Industry 4.0 Factory Scaling Evaluator with Mastra AI
Scaling Industry 4.0 pilots across multiple plant locations fails due to heterogeneous data formats and unstandardized metrics. Build an automated factory scaling agent with Mastra AI to normalize edge telemetry and evaluate deployment readiness. Achieve complete schema alignment across multi-plant OPC UA servers.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a Mastra AI workflow to audit multi-plant data schemas and assess readiness for enterprise Industry 4.0 rollouts.
How work is evaluated
Verify Mastra AI agent workflow output for plant telemetry standardization and readiness score calculation.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
schema_normalization_pass
Correctly maps raw plant tag names to canonical forms
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
readiness_score_accuracy
Accuracy of calculated readiness index • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Create a Mastra AI project with agents and workflows for plant schema evaluation
Normalize heterogeneous OPC UA and MQTT telemetry data payloads
Compute factory digital readiness metrics using structured agent steps
Generate multi-plant gap analysis reports
Reference links and supporting material
Raw tag exports from 10 different factory sites with varying tag naming schemes.
How this agent runs
Verify Mastra AI agent workflow output for plant telemetry standardization and readiness score calculation.
Challenge input
JSON containing plant_id, raw_tags, and connectivity_type
Mastra AI
TypeScript framework for modular AI workflows.
Evaluated output
JSON with normalized_tags, readiness_score, and missing_requirements
- Correctly maps raw plant tag names to canonical forms
- Accuracy of calculated readiness index • target: 0.95 • range: 0-1
- Benchmark: ISA-95 Schema Normalization Benchmark
- Readiness Score Accuracy target: 0.95
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- Mastra AI · Required
Evaluation contract
- schema_normalization_pass · Weight 1
- readiness_score_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.