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Equipment Maintenance Knowledge Copilot with Mastra AI and o3

Factory maintenance teams face severe knowledge gaps when troubleshooting specialized machinery during unexpected downtime events. You will build an equipment troubleshooting copilot using Mastra AI and the OpenAI o3 model to parse complex technician notes, schematics, and sensor anomalies into actionable step-by-step repair guides. The copilot must reduce maintenance query response latency to under 2 seconds.

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What you are building

Develop a TypeScript-based Mastra AI workflow leveraging OpenAI o3 reasoning capabilities to close factory floor maintenance knowledge gaps.

How work is evaluated

Evaluation

Evaluates response precision, step ordering, and execution speed of the Mastra AI troubleshooting copilot.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

loto_safety_inclusion

Ensures lockout/tagout (LOTO) safety steps are included for hardware maintenance tasks.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

response_latency_seconds

Time to return structured resolution steps • target: 1.8 • range: 0-5

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Build modular Mastra AI agent workflows using TypeScript and structured schema tools

  • Incorporate OpenAI o3 reasoning model calls for high-accuracy multi-step root-cause deduction

  • Create semantic indexing pipelines for handwritten technician work order histories

  • Integrate real-time equipment error codes with dynamic step-by-step resolution paths

Resources

Dataset notes

Dataset of anonymized robot controller error codes, historic shift logs, and repair resolution times.

Challenge setup

Evaluates response precision, step ordering, and execution speed of the Mastra AI troubleshooting copilot.

Challenge input

JSON with fault_code, machine_type, and user_symptom_description

Mastra AI

Required framework assignment.

o3

Required tool assignment.

Evaluated output

JSON containing structured steps (list of actions), required_tools (list), and estimated_downtime_mins

Evaluation checks
  • Ensures lockout/tagout (LOTO) safety steps are included for hardware maintenance tasks.
  • Time to return structured resolution steps • target: 1.8 • range: 0-5
Configured evaluation evidence
  • Benchmark: Industrial Copilot Maintenance Benchmark
  • Response Latency Seconds target: 1.8
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Mastra AI · Required
  • OpenAI · Optional
Policy Serving
  • o3 · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • loto_safety_inclusion · Weight 1
  • response_latency_seconds · Weight 1

Metrics

  • response_latency_seconds · Target 1.8

Benchmarks

  • Industrial Copilot Maintenance Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with fault_code, machine_type, and user_symptom_description

    Output: JSON containing structured steps (list of actions), required_tools (list), and estimated_downtime_mins

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · TypeScript Agent Framework
  • o3 · Reasoning Model

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Equipment Maintenance Knowledge Copilot with Mastra AI and o3