CharterSense / CharterSense v2
AI-powered maritime chartering intelligence & real-time voyage profitability analytics
CharterSense is an enterprise-grade maritime chartering intelligence and analytics platform built for bulk carriers, tanker operators, shipping companies, and chartering desks. It converts raw voyage/commercial/operations/laytime data into real-time profitability visibility (Estimated vs Actual vs Accounted P/L), spot-loss prevention with AI-driven root-cause categorization, and partner/route/port intelligence—so teams stop spending 40+ hours a week in spreadsheets and instead catch losses early, improve voyage estimation accuracy, optimize charterer/broker decisions, and reduce demurrage exposure.
Cost of blind spots and inefficiencies in maritime chartering economics.Cost of blind spots and inefficiencies in maritime chartering economics.
What it does
- 01Profitability Intelligence (fleet-wide P/L + deep drilldowns)
- 02Spot Loss Analysis (rule-based identification + trend + worst performers)
- 03Variance Analysis (Estimated vs Actual vs Accounted, by phase + category)
- 04Route & Port Intelligence (heatmaps, scorecards, and route economics)
- 05Charterer & Broker Intelligence (fairness, execution, consistency, pairing)
- 06AI-Powered Analytics using Google Gemini for voyage loss reason categorization with confidence scoring and auditability
Features
Real-time P/L Dashboards
Estimated, Actual, and Accounted metrics with advanced filtering and drilldowns
Spot Loss Detection
Rule-based identification + top loss voyages + loss trends + worst performers
AI Root-Cause Categorization
Voyage loss reason analysis with standardized categories + confidence scoring + audit trail
Charterer & Broker Intelligence
Performance matrix, fairness index, execution score, market behavior trends, and pairing analysis
Route & Port Analytics
Trade lane heatmaps, port scorecards, interactive map visualization
Laytime Dashboards
Demurrage/despatch trends, efficiency scoring, approval status tracking
Variance Analysis
Estimated vs Actual vs Accounted, by phase + category with systematic bias detection
Period-wise Intelligence
Monthly/quarterly/yearly reporting with comparisons and best/worst periods
By the numbers
Cost of blind spots and inefficiencies in maritime chartering economics
Typical manual spreadsheet analysis time by operations/commercial teams
Typical improvement via spot loss prevention + root-cause analytics
Typical demurrage/despatch optimization impact
When you'd use this
Spot Loss Post-mortem
Auto-identify qualifying loss voyages (TC, completed, ≤ -$20K). AI categorizes root causes (e.g., load/discharge delay, bad weather, documentation, bunker, etc.). Compare against historical patterns by route/charterer/vessel to prevent repeats.
Voyage Estimation Calibration
Track variance by route, port, cargo, vessel type, and voyage phase. Highlight systematic bias (e.g., optimistic waiting time estimates). Feed learnings back into estimation models and commercial decision-making.
Partner Selection Optimization
Score charterers on fairness index + execution efficiency. Rank brokers on value-add and consistency. Simulate better charterer-broker pairings using historical outcomes.
Port and Trade Lane Optimization
Use trade lane heatmaps to spot profitable flows. Benchmark ports via performance scorecards (waiting days, port days, net P/L). Improve deployment decisions using route economics and stability metrics.
Specs
- Data Store
- PostgreSQL + Drizzle ORM (type-safe access)
- Authentication
- Clerk authentication with SSO support
- Security
- Role-based access control (RBAC), multi-tenant architecture, audit logging
- AI Services
- Google Gemini API (Gemini 2.5 Flash for voyage loss analysis)
- Performance
- Sub-second query response times (< 1 second for standard queries)
- Data Capacity
- 100K+ voyage records/year; 50+ concurrent users; 5+ years historical data recommended
- Deployment
- Docker containerized; cloud/on-prem/private cloud/hybrid; Kubernetes-ready; CI/CD compatible
- Geocoding
- Nominatim / Mapbox