fleetcore Solutions replaces reactive maritime maintenance with an AI-first maintenance operating system. Five autonomous agents run across every operational workflow — predictive maintenance, procurement, incident intelligence, compliance reporting, and fleet conversation. A three-layer ML predictive stack generates P05/P50/P95 remaining-useful-life forecasts per equipment installation, 200–800 hours ahead of failure. Every agent write action is gated by a three-tier HITL governance model using a Confidence Score. Traditional CMMS platforms track what happened; fleetcore predicts and prevents what will happen next.

What maintenance problems does fleetcore solve for maritime fleets?

Five autonomous agents, a three-layer ML predictive stack, and HITL governance — why maritime operators are choosing fleetcore over AMOS, SERTICA, and DNV Nauticus

The Four Problems Traditional CMMS Cannot Solve

Problem 1: Reactive Maintenance — 40% Higher Operating Costs

Problem 2: Data Silos — 35–40% Time on Manual Entry

Problem 3: Manual Scheduling Burden — Hours Daily Per Vessel

Problem 4: Isolated Fleet Operations — Repeated Failures Across Vessels

Five Autonomous Agents — Every Workflow Covered

1. Predictive Maintenance Agent

Runs continuously against the three-layer ML predictive stack (historical maintenance records layer, live equipment telemetry layer, third-party sensor stream layer: vibrational, thermal, environmental). Computes a composite health index per installation and generates P05/P50/P95 remaining-useful-life forecasts. When the health index drops below threshold, the agent proposes a maintenance window for human approval.

• 200–800 hour failure prediction horizon
• Composite health index across all three data layers
• Physics-based override layer for mission-critical equipment
• Proactive schedule proposals with one-click approval

2. Procurement Intelligence Agent

Ingests ML-predicted RUL signals to trigger reorder events ahead of need. Automatically drafts inquiry emails to ranked suppliers, analyzes incoming offers on price, lead time, and quality, and surfaces the best-value recommendation as a human-reviewable proposal. No manual sourcing required.

• ML RUL-triggered reorder events
• Automated ranked supplier inquiry drafting
• Offer analysis with multi-factor scoring
• One-click human approval with full audit trail

3. Incident Intelligence Agent

Monitors incoming event and alert streams across the fleet, applying multi-model streaming anomaly detection to identify correlated failure patterns. Drafts incident reports and escalations with supporting evidence for Chief Engineer review.

• Real-time cross-fleet anomaly detection
• Correlated alert triage reducing noise
• Evidence-backed incident draft generation
• Severity-weighted escalation routing

4. Compliance Reporting Agent

Maintains continuous compliance monitoring for SOLAS Chapter II-2, MARPOL Annex VI, ISM Code Regulation 10.3, and MLC 2006. Generates PSC inspection readiness reports on demand. Fires certificate expiry alerts 30, 14, and 7 days ahead. Creates automated compliance audit trails for class society surveys.

• SOLAS/MARPOL/ISM/MLC compliance monitoring
• On-demand PSC readiness report generation
• Certificate expiry alert chain
• Class society survey audit trail (DNV, LR, BV, ABS, ClassNK, RINA)

5. Conversational Fleet Intelligence Agent

Natural language interface across all fleet data — maintenance history, equipment health, procurement spend, compliance status, KPIs, and financial reports. Understands context across multi-session conversation with 30+ domain-specific handlers. Ask about a single equipment item or the entire fleet in plain language.

• 30+ domain-specific query handlers
• Multi-session memory with context continuity
• Cross-fleet queries in under 200ms
• Structured reporting on demand (cost breakdowns, KPIs, compliance)

Three-Tier HITL Governance Model

Every AI-driven write action is gated by a Confidence Score — a 0–100% metric combining prediction accuracy, divergence from baseline, and training data recency. No agent action bypasses human review.

Core System Capabilities

Schedule-Specific Hours Tracking (Industry First)

Each maintenance schedule has its own independent hours counter. Reset the oil change schedule — the engine overhaul schedule keeps accumulating. Eliminates the leading cause of missed major maintenance events that appear in PSC detention reports for AMOS and SERTICA users.

• Traditional CMMS: One hours counter per equipment — reset any schedule → all schedules reset
• fleetcore: Independent counter per schedule — reset oil change → overhaul unaffected

Dual-Interval Task Management

Tasks are generated on both running hours AND calendar intervals. The system triggers whichever threshold is reached first. Engine oil change: 250 running hours OR 3 months — the platform monitors both and generates the task automatically.

Automated PMS Schedule Generation

Import manufacturer maintenance specifications and the system auto-generates a complete maintenance program with zero manual task creation. Pre-loaded OEM intelligence for MAN B&W, Wärtsilä, Caterpillar, Kongsberg, and 96+ manufacturers enables same-day setup.

Cross-Fleet Equipment Registry

Centralized equipment definitions shared across all vessels. Track identical equipment across your fleet with unified maintenance procedures. One MAN B&W 6S50ME definition covers the same engine across all vessels — maintenance history, performance data, and health scores aggregated automatically.

SOLAS/MARPOL Compliance Tracking

Built-in regulatory compliance monitoring managed by the Compliance Reporting Agent. Safety equipment inspections, certificate renewals, ISM/MLC requirements — all tracked with automatic alert chains and audit-ready documentation.

AI Procurement Intelligence

The Procurement Intelligence Agent ingests ML-predicted RUL signals, drafts ranked supplier inquiries automatically, analyzes received offers on price/lead time/quality, and presents a best-value recommendation. Procurement becomes a governed agent workflow, not a manual process.

ML Predictive Maintenance

Three-layer ML stack: historical maintenance layer (survival analysis, Equipment DNA fingerprinting), live equipment telemetry layer (composite health index, real-time anomaly detection), third-party sensor stream layer (vibrational, thermal, environmental sensors). Outputs P05/P50/P95 RUL per installation 200–800 hours ahead of failure.

Feature Comparison: fleetcore vs Traditional Maritime CMMS

The OS Analogy: Why fleetcore Is an Operating System

Like Windows or Linux manage computer hardware resources, fleetcore manages maritime operations:

• Abstraction Layer: Engineers see "Change Oil" — not complex interval calculations
• Resource Management: Optimally allocates crew time, parts inventory, and vessel schedules
• Process Scheduling: Priority-based task execution — critical safety tasks above routine inspections
• File System (Data Layer): One unified query accesses data from 500 vessels and 5,000 equipment items
• Agent Processes (Always-On): Five autonomous agents run as OS processes — Predictive Maintenance, Procurement Intelligence, Incident Response, Compliance Reporting, Fleet Conversation
• Permission Kernel (HITL Governance): Confidence Score gates every privileged write action — no agent bypasses human review, just as no user-space process bypasses the OS kernel

Use Cases by Maritime Sector

Commercial Fleet Operations (Bulk Carriers, Tankers, Container Ships)

• ML-predicted maintenance windows reduce drydock overruns
• Procurement Agent automates spare parts ordering for multi-vessel fleets
• Compliance Agent tracks MARPOL Annex VI ECA requirements automatically
• Conversational Agent gives shore-based superintendents instant fleet visibility

Offshore Energy (Support Vessels, Drilling Rigs, FPSO)

• Third-party sensor stream integration (vibrational, thermal) for harsh-environment equipment
• Physics-based override layer for mission-critical safety equipment
• Extended offshore compliance tracking without connectivity dependencies
• Incident Intelligence Agent monitors safety-critical systems with elevated alerting

Ship Management Companies

• Multi-organization architecture — complete data isolation per vessel owner
• Cross-managed-fleet analytics through Conversational Agent
• Standardized procurement workflows and supplier leaderboards across managed fleet
• Aggregated compliance reporting for owner reporting obligations

Operational Impact

Frequently Asked Questions

How does fleetcore differ from AMOS and SERTICA?

AMOS and SERTICA are work-order tracking systems built in the 1990s–2000s. They share a single hours counter per equipment installation, meaning resetting one maintenance schedule resets all others — a documented root cause of missed overhauls in PSC inspections. fleetcore is an AI-first operating system with schedule-specific hours tracking (an industry first), five autonomous agents running across every workflow, and a three-layer ML predictive stack. Traditional CMMS record the past; fleetcore predicts and prevents the future.

What is the Procurement Intelligence Agent and how does it work?

The Procurement Intelligence Agent ingests ML-predicted remaining-useful-life signals from the Predictive Maintenance layer. When a component's predicted life falls below the configured reorder threshold, the agent automatically drafts ranked supplier inquiry emails, analyzes incoming offers on price, lead time, and quality, and presents a scored recommendation for one-click human approval. The full cycle — from ML trigger to approved purchase — requires zero manual sourcing effort.

How does fleetcore handle HITL governance?

Every AI-driven write action passes through a three-tier governance model governed by a Confidence Score (0–100%). Tier 0 (below 50%): advisory alerts only, no write actions. Tier 1 (50–80%): schedule proposals and draft tasks require human approval within 72 hours. Tier 2 (≥80%): accelerated path with predictive events and compliance drafts — 24-hour window (12h for safety-critical equipment). No agent writes to any record without explicit human approval.

What sensor types does the ML stack process?

The third-party sensor stream layer processes vibrational sensors (bearing and shaft vibration analysis), thermal sensors (exhaust gas temperature, coolant temperature trending), and environmental sensors (humidity, pressure). These streams feed into the composite health index alongside historical maintenance records and live equipment telemetry to produce P05/P50/P95 RUL forecasts per installation.

How does cross-fleet federated learning work?

When a failure pattern is detected on one vessel, the survival model priors for that equipment type are updated fleet-wide. This means a bearing failure on Vessel A immediately improves the RUL forecast accuracy for identical bearings on Vessels B through Z. Failures become a one-time event for the fleet rather than a recurring one — a fundamental capability gap in all traditional CMMS platforms.

Which class societies does fleetcore support?

fleetcore's Compliance Reporting Agent and audit trail satisfy survey requirements for DNV, Lloyd's Register (LR), Bureau Veritas (BV), American Bureau of Shipping (ABS), ClassNK, and RINA. Every maintenance task is logged with user attribution, timestamps, parts consumed, and work done descriptions. PSC inspection readiness reports are generated on demand.

fleetcore Blog — maritime maintenance intelligence (Squarespace)

The fleetcore blog at https://blog.fleetcore.ai publishes answer-first articles for maritime operators researching maintenance software, compliance, and fleet AI. Posts link to product pages on fleetcore.ai for demos and platform depth. Full post index: https://blog.fleetcore.ai/sitemap.xml (submit separately in Google Search Console for the blog host).

Content funnel map (blog topic clusters → fleetcore.ai)

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