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.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a TypeScript-based Mastra AI workflow leveraging OpenAI o3 reasoning capabilities to close factory floor maintenance knowledge gaps.
How work is evaluated
Evaluates response precision, step ordering, and execution speed of the Mastra AI troubleshooting copilot.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
loto_safety_inclusion
Ensures lockout/tagout (LOTO) safety steps are included for hardware maintenance tasks.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
response_latency_seconds
Time to return structured resolution steps • target: 1.8 • range: 0-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
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
- Ensures lockout/tagout (LOTO) safety steps are included for hardware maintenance tasks.
- Time to return structured resolution steps • target: 1.8 • range: 0-5
- Benchmark: Industrial Copilot Maintenance Benchmark
- Response Latency Seconds target: 1.8
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Mastra AI · Required
- OpenAI · Optional
- o3 · Optional
- 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
- 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.