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Ocean-Tested • 50,000+ Nautical Miles

Shimbiri Marine Intelligence

Enterprise-Grade AI Platform for Marine Operations

Processing 33GB Monthly Telemetry Data Per Vessel
33GB
Monthly Data/Vessel
Continuous learning
100+
Simulated Fleet
Full-scale testing
94.7%
Prediction Accuracy
12-72hr advance
50Hz
Sampling Rate
Real-time processing
2M+
Documentation
Fully searchable
<50ms
Response Time
Edge inference

Enterprise System Architecture

Distributed, scalable, production-ready infrastructure

🛡️

Data Ingestion Layer

High-throughput NMEA 2000/0183 processing with redundant pathways and automatic failover. Handles 50Hz sensor streams from 200+ data points per vessel.

ProtocolCAN Bus/TCP
Throughput1.2k msg/s
Buffer8192 frames

Stream Processing

Apache Kafka-based event streaming with Rust processors for ultra-low latency. Distributed across multiple availability zones with automatic replication.

PlatformKafka 3.5
Retention33GB/vessel
CompressionZstandard
🧠

AI Orchestration

Multi-model architecture routing queries to specialized AI engines. Gemini for manuals, Claude for reasoning, GPT-4 for conversation, Llama for edge.

Models6 Active
Context2M tokens
Latency<50ms edge
💾

Vector Database

Pinecone-powered semantic search across 2M+ pages of technical documentation. Distributed pods with automatic scaling and sub-second retrieval.

PlatformPinecone
Dimensions1536
Pods4 regions
📊

Time Series DB

TimescaleDB for high-resolution telemetry storage with automatic compression and retention policies. Optimized for marine operational patterns.

PlatformTimescaleDB
Retention2 years
Compression10:1 ratio
🚀

Edge Deployment

Containerized edge computing for vessel-local processing. Quantized Llama 3.1 70B for offline capability with automatic cloud sync when connected.

RuntimeDocker/K8s
ModelLlama 70B Q4
SyncAutomatic

Self-Evolving Neural Architecture

The crown jewel of marine AI - a system that learns from every wave, every engine cycle, every journey

33GB
Monthly Learning/Vessel
158M
Patterns Analyzed
47
Failure Types Mastered
Continuous Evolution

Layer 1: Sensory Ingestion

📡
NMEA Stream
50Hz
🌊
Environmental
Real-time
⚙️
Mechanical
200+ points
📍
Navigation
1m accuracy

Layer 2: Pattern Recognition

🔍
FFT Analysis
Harmonics
📊
Anomaly Detection
94.7% acc
🔄
Trend Analysis
Predictive
🎯
Correlation
Multi-var

Layer 3: Knowledge Synthesis

🧠
Neural Fusion
6 Models
💡
Insight Generation
Real-time
🔮
Prediction
12-72hr
📚
Context
2M docs

Layer 4: Evolutionary Adaptation

🚀
Self-Improvement
24/7
🌐
Fleet Learning
Distributed
♾️
Feedback Loop
Continuous
Edge Update
Automatic

Knowledge Accumulation Over Time

Day 1
Baseline patterns
Week 1
Vessel personality learned
Month 1
33GB patterns analyzed
Month 6
Predictive mastery achieved
Year 1
400GB knowledge base
Ongoing
Infinite improvement

Real-Time Data Pipeline

From sensor to insight in milliseconds

Ingestion
50Hz
NMEA 2000 CAN bus data capture
Normalization
SignalK
Unified data format
Streaming
Kafka
Distributed processing
Analysis
ML/AI
Pattern detection
Storage
33GB/mo
Time-series retention
Action
<50ms
Real-time alerts

System Performance Metrics

Validated through 2 years of ocean testing

3.3TB
Total Data Processed
47
Failure Patterns
12-72hr
Advance Warning
99.2%
Uptime SLA
200+
Data Points/Vessel
6
AI Models

Technology Stack

Production-tested, enterprise-grade components

AI & Machine Learning

Gemini Flash 2.0 (2M context)
Claude 3.5 Sonnet
GPT-4 Turbo
Llama 3.1 70B (Quantized)
TensorFlow 2.14
PyTorch 2.0

Data Infrastructure

Apache Kafka 3.5
TimescaleDB
Pinecone Vector DB
Redis Cache
PostgreSQL 15

Marine Systems

NMEA 2000 / 0183
SignalK Server
CAN Bus Interface
Modbus TCP/RTU
Seatalk NG

Infrastructure

Railway Platform
Kubernetes 1.28
Docker Swarm
GitHub Actions CI/CD
Prometheus + Grafana

Built by sailors who code. Not coders who sail.
Every line tested on the ocean. Every pattern learned from real failures.