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Overview / Session Notes / Janet — Machine Maintenance

Session Notes: Janet — Machine Maintenance

2026-06-03 · Janet, with Nate St. Pierre · ~10 minutes, on-site

Overview

This was a short, focused session — about ten minutes — that Janet initiated herself. She raised machine maintenance as a possible efficiency improvement before Nate brought it up. The conversation moved quickly from describing the problem into a generative brainstorm about what an AI-powered maintenance system could look like. Janet had clearly been thinking about this already; the session mostly gave her a space to reason it through out loud.

The Problem

Merrick runs roughly a dozen different machine types on the production floor, each with its own maintenance requirements and schedule. Some machines require daily maintenance; others have weekly, monthly, six-month, or yearly steps. The production team handles the maintenance, but there's no consistent system for logging what was done, when it was done, and by whom.

The consequences Janet named: when a machine has a problem, there's no historical record to look back at for patterns. It's hard to tell whether a breakdown was preventable, or whether a recurring issue is genuinely recurring. Accountability is a gap as well — when maintenance doesn't happen, there's no trail to understand why or to follow up on it.

The UV printers are the highest-stakes machines in this regard. There are three of them, and they require daily cleaning — sometimes multiple times a day when they're running continuously — to keep the ink heads from clogging. Each print head costs roughly $2,000. When a UV printer goes down, it's a significant operational problem. The lasers have their own maintenance needs (weekly cleaning, sensitivity to dust particles) but are somewhat less critical in terms of failure cost.

Beyond logging, troubleshooting is also manual and fragmented. When something goes wrong with a machine, operators rely on whatever documentation can be found — manufacturer manuals that exist somewhere as PDFs — plus individual experience. Janet has done work to gather and organize this material, but searching a 200-page manual in the middle of a problem is slow and frustrating.

There's also a maintenance calendar of some kind, but it isn't functioning well as an accountability tool. The daily maintenance tends to happen. The less-frequent steps — monthly, six-month, yearly — are the ones that are easy to miss.

What an AI-Powered Maintenance System Could Look Like

Janet's thinking and Nate's sketching converged on a concept for a unified maintenance system — roughly described as a single app accessible from shared computers on the production floor, designed around how operators actually work.

The core elements discussed:

  • Machine-specific AI assistant. Each machine type would have its own AI-powered interface, loaded with that machine's manufacturer manuals, past maintenance notes, and any recorded operator knowledge. An operator who runs into a problem could ask a question in plain language and get an answer drawn from the actual documentation — without searching through a manual. The assistant would know which machine it is and respond in that context.
  • AI-written maintenance logs. Rather than requiring operators to stop and write log entries, the system would let them describe what they did verbally — and write the log for them. Nate demonstrated how his own system works this way: he talks through what he's doing while he's doing it, and the AI captures and organizes it. Janet's response was immediate: "That sounds amazing." The goal is for maintenance logging to become close to zero additional effort — the operator narrates, the system records.
  • Maintenance calendar. The app would show today's maintenance calendar on opening — which machines need what, based on their schedule. This creates a clear daily checklist and a record of what was completed.
  • Operator training recordings. Janet mentioned that stored video walkthroughs of how maintenance procedures are performed would also be valuable, especially for newer operators who haven't yet learned a particular process. Those recordings, combined with the AI Q&A, would reduce dependence on whoever happens to have the most experience with a given machine.

Janet acknowledged openly that the full vision is a significant build, and that the question of what to actually prioritize and fund is above her. But she was clear that machine maintenance matters — and that the UV printers in particular make this more than an organizational-convenience problem.

Context on Nate's System

Nate briefly described his own AI operating system as a real-world example of voice-driven AI logging — specifically how he uses a voice-transcription tool combined with Claude to capture notes, update records, and manage his workflow by talking rather than typing. Janet was genuinely interested. The demonstration helped her see voice-logging as something that already exists and works, not just a hypothetical. She mentioned she'd like to see a live demo during the Day 6 training session.

Follow-Ups

  • Collect manufacturer PDFs for each machine type from Janet or from wherever they're currently stored.
  • Confirm what shared systems exist on the production floor (browser access, installed software, etc.).
  • Determine whether Calloway and Sandra see machine-maintenance infrastructure as a priority investment.
  • Ask Janet whether any maintenance video walkthroughs have ever been recorded.