StatsMonitor

Result

Root cause identified as a packaging defect. The gripper was tearing the top of the customer's incoming boxes during pick attempts because the boxes had a hole on the top face that was breaking the vacuum seal. Findings reported to my manager and the customer support team. The EOAT Reliability Watchlist Operating Guide that came out of this work covers severity levels, watchlist categories, week-over-week trend badges, evidence-collection procedures, and escalation paths. The senior technical writer reviewed it, the customer support team adopted it, and I handed both the guide and its underlying pipeline off to a coworker before leaving the internship.

Result

The watchlist surfaces controllers in severity order, top-down. Each row tells the customer support team three things at a glance: how severe (color), what kind of issue triggered it (driver category), and what to investigate (the SOP-defined recommended action). Because the score is peer-relative, "most extreme right now" is the natural sort key, and the team doesn't have to compare absolute thresholds across controllers operating in different conditions.

Result

The watchlist now surfaces two complementary signals per controller: severity (which controller is the worst right now) and trajectory (which controllers are deteriorating). The customer support team uses both together. A "Degrading yellow" controller can take priority over a "stable red" one because the trajectory tells the team which problems are still actively unfolding. The trend badge feeds the same SOP-driven escalation paths as the severity color, so a controller showing Degrading for two consecutive weeks triggers a defined escalation step in the operating guide.

Result

The customer support team gets two clearly separated signals on each card. Severity (color and category) tells them whether the controller is unhealthy. Coverage tells them whether the data they're looking at can be trusted at all. A row of green and red squares on each card shows which days had required stats over the last 7 days, with a count below it. When a controller's data is unreliable, that surfaces as its own watchlist row pointing at the right team to investigate, not as a misleading performance signal. Time-breakdown bars and trend tables draw blanks rather than zeros where the data isn't there, so the report stays honest about what it knows.

Result

The pipeline runs nightly without intervention. Failures are non-fatal at the right granularity: a single bad controller is logged to a failure CSV and the rest of the run completes. Partial failures don't silently advance state and create gaps in the historical record. A killed process leaves a clean filesystem because the source-of-truth files are atomically written. The full historical archive is reconstructable because every downstream artifact is a function of bronze.

Result

The customer support team has both halves of a diagnostic system: an alert layer (the watchlist) that tells them which controllers to look at, and an investigation layer (the panel) that lets them actually look. The team has used the side-by-side comparison feature to compare two gripper systems against each other directly. The panel was also portable enough to carry into a separate project, where I used it during EOAT validation testing to compare a baseline suction cup against a new model under field conditions over multiple weeks.

Result

The guide was adopted by the customer support team. My coworker mentioned wanting to put it up on a monitor in the office so the team can see all the controllers at once during their day. The guide is one of two things that made the system actually transferable when I left: the second was the codebase itself. I handed both to a coworker before my last day and made sure he had the pipeline running on his end. The system continues to surface anomalies on its own daily, and the team has a written playbook for what to do with them.