Maintenance with AI mentoring

Your data knows why machines stop. Someone just has to read it.

Your maintenance system stores every work order, every stop and every part replaced. In three levels, with the modules your plant needs, I build with you the second brain of maintenance: the history becomes dashboards, plans and decisions, from the manager to the team and to the plant.

3mentoring levels

12modules in the hive

1tailored proposal

The whole history is there. Nobody has time to read 150,000 work orders.

What's missing is turning the history into answers: which machine to stop firefighting on, which plan to change, which part to buy. Generic AI doesn't help here: it doesn't know the plant, doesn't read the data and guesses when context is missing.

On your own

Spreadsheets and firefighting

Reports built by hand every week
Failure and cause left blank, analysis that never happens
Preventive on the calendar, corrective in charge
Storeroom full of the wrong parts
Budget defended on gut feeling

With someone who has built it

Dashboard, method and numbers

A dashboard reading the data, no one building spreadsheets
Downtime Pareto and failure analysis with method
Plans by asset family and a 52-week leveled crew
Min/max based on actual consumption
Cost of indecision in BRL, with source and date

In two real industrial databases, read-only and anonymized (October 2026): 13% of machines accounted for 80% of downtime hours, 52% of downtime work orders had no service type recorded and there were R$ 2.7 million in idle stock items, with no min/max set.

Each level sets how far the twin goes in the plant and how much I do for you.

Level 1

Clone

The maintenance manager's twin. We build it together.

4 weeks · online · one 1-hour session per week

Memory and rules: the plant, the areas, the critical assets and the in-house vocabulary
Your work order and service request data in the twin, via export or read-only connection
Weekly maintenance report ready on Monday
planning meeting agenda and minutes with an action plan
First failure analysis on the family that weighs most, with time measured before and after

Level 2

Team

The digital maintenance team and the dashboard management reads. I build, you operate.

3 months · 1 day at the plant · biweekly sessions

Everything in Clone, plus:
Digital professionals: Reliability, Planner, Scheduler, Materials, Costs and Indicators
Maintenance dashboard: assets, materials, preventive, people, costs and failures
Pareto of downtime hours, MTBF and MTTR by machine
Failure analysis with Pareto, 5 Whys, Ishikawa, FMEA and Weibull, closing in a 5W2H plan
Cost of indecision in BRL and maintenance cost projection

Level 3

Plant

The plant's maintenance restructured on top of the data. I build it for the plant.

6 months · week at the plant · weekly sessions · monthly committee

Everything in Team, plus:
Asset hierarchy and a criticality workshop per area
Plans by family, with failure, cause and action ready to load
Spare parts with min/max based on 12 months of consumption
52-week preventive plan, with leveled man-hours and the crew size that makes the numbers work
Weekly ritual, action plan with owner and deadline, and a living project document

A dashboard management reads and a digital team that answers.

The dashboard is born from your data: collection is read-only, the data stays with your company and every number shows where it came from. Next to it, the digital professionals answer the questions maintenance asks every week, separating fact, hypothesis and recommendation.

Reliability“Why does Asset 1 stop every week?”

Planner“What changes in this family's plan?”

Scheduler“Can the crew handle next week?”

Materials“Which part has been idle for over a year?”

Costs“What does not changing the plan cost?”

Indicators“How did availability close this month?”

Nothing is written to the database unless you say so.

Modules that fit into any level.

Every plant feels the pain in a different place: in downtime, in inventory, in the plan or in the budget. You choose the level and fit in the modules your maintenance needs. The proposal comes out with that composition, nothing more.

Downtime and availabilityPareto, MTBF and MTTR by machine

Failure analysis5 Whys, Ishikawa, FMEA and Weibull

Asset criticalityMatrix and workshop per area

Asset hierarchy and master dataFrom site to component, ready to load

Plans by familyTasks linked to the failure mode

52-week preventive planRoutes, leveled man-hours and crew

Materials and spare partsMin/max based on consumption

Backlog and planningQueue, priority and overdue work orders

Costs and budgetCost by area and multi-year projection

Data qualityWhat the data lacks for AI to get it right

Team trainingWorkshop with technicians and planners

Monthly continuityReview and tuning after the mentoring