Case study · Reliability & maintenance intelligence
Kaysee. Scoped, built, and operated by Cortland.
Kaysee is a production reliability platform — a Claude-native System of Intelligence that sits on top of the maintenance data industrial operators already own. Cortland designed its cognitive architecture, built the multi-agent orchestration layer, integrated it with the CMMS and historian systems our clients actually run, and ships it into production every week.
This page is the build story. For the product, the industries Kaysee covers, and a live demo, visit kaysee.ai.
At a glance
A production AI platform, delivered end to end.
Cognitive architecture
Recall · Think · Act
A patent-pending framework for reasoning across operational history, current state, and next action.
Orchestration
Multi-model, not single-vendor
Claude, ChatGPT, and Grok routed as specialized reasoning engines behind a single operator experience.
Coverage
Eight industries, one platform
From aviation to marine, built once and specialized per domain. Full list on kaysee.ai.
Operator outcomes
Downtime down. FMEA hours down.
Production deployments measurably reduce unplanned downtime and the hours operators spend assembling FMEAs and RCAs.
The problem
Every plant already has the data. Almost none of it is intelligence.
Industrial operators sit on decades of work orders, inspection records, failure modes, operator notes, historian tags, and maintenance PMs. The CMMS is where all of it lives — and where most of it stays. It is a System of Record, not a System of Intelligence.
The reliability engineer doesn’t need another dashboard. They need the last three RCAs that looked like this one, the related work orders across the sister unit, the historian trace for the bearing that failed in 2022, and a recommendation they can argue with — in under a minute, from the same window.
That is the gap Kaysee was built to close.
What Kaysee is
A reliability co-pilot that thinks alongside your team.
Kaysee reads what the CMMS, historian, and field notes already say and answers the questions reliability teams actually ask. It recalls what the plant has seen before, reasons about what’s happening now, and acts by drafting the work order, the FMEA update, or the RCA — for an operator to approve.
The three-step loop
Recall
Pulls the operational memory — past failures, related assets, prior decisions — across the systems the client already runs.
Think
Routes the question to the right model for the task. Long-form reasoning, structured extraction, and real-time synthesis each have a home.
Act
Drafts the artifact an operator needs next — work order, FMEA entry, RCA, inspection plan — and hands it back for human approval.
How Cortland built it
Operator-grade AI architecture, delivered by a team that understands both sides of the fence.
Cognitive architecture
Recall – Think – Act is the spine. Every Kaysee interaction flows through operational memory, a reasoning step, and a reviewable action — not a chat window sitting next to a CMMS.
Multi-model orchestration
Claude anchors reasoning and long-context review. ChatGPT and Grok are routed in where their strengths fit. Operators never see the seams — they see one assistant that gets the answer right.
Enterprise data integration
Kaysee integrates with the CMMS, historian, fleet telematics, and document repositories our clients already run. Memory is persisted across sessions so the platform compounds over time.
Agentic workflows
Onboarding, asset enrichment, and RCM plan drafting run as queued background jobs — not synchronous chat turns. Scale without stalling the operator on the other end.
Production stack
Node.js services, a React operator interface, Redis-backed work queues, document and vector storage, containerized deploys — running on Google Cloud and shipped through Cortland’s standard CI.
Human in the loop
Every draft Kaysee produces is reviewable, editable, and auditable. No action is taken on plant systems without an operator signing off — the only way AI earns its seat in a reliability org.
Outcomes
Less downtime. Fewer hours buried in spreadsheets.
Kaysee customers are measuring the same two things we hear reliability leaders name every time we scope an engagement: how often the plant surprises them, and how long it takes the team to turn a surprise into a decision.
Reduction in unplanned downtime
Kaysee surfaces failure patterns across assets and sites before they propagate — so the next outage isn’t the first time the team hears about the failure mode.
Fewer hours spent on FMEA and RCA
Failure mode assembly and root cause analysis are drafted by Kaysee against the plant’s actual history. Engineers review, refine, and approve rather than start from a blank page.
Institutional memory that compounds
Every RCA, every work order, every operator note becomes part of the recall layer for the next question. The platform gets more valuable the longer it runs.
Named customer outcomes will publish as deployments complete and clients approve the story. For sample customer numbers, see the case highlights on kaysee.ai.
Why Cortland
The only group that lives between industrial settings and frontier tech.
Kaysee exists because the standard consulting answers don’t reach the plant floor. Industrial consultants understand the work but can’t ship software. Coastal AI firms can ship but don’t know why the reliability engineer rejects the tool that ignores criticality ranking.
Cortland has been inside Fortune 500 industrial operations since 2018. We know turnaround schedules, PSM requirements, the CMMS quirks nobody writes down, and the difference between a good FMEA and one that gets shredded in the next audit. And we build production AI on Claude every week.
Kaysee is what happens when those two things are the same team.
Want this for your operation?
Kaysee is one of the production platforms we’ve already shipped. The next one is yours. If you have a reliability, inspection, compliance, or operations problem that Claude should be solving, let’s scope it.