The Technology Behind AquaCORTEX

A deep dive into the technical architecture, AI capabilities, and system modules that comprise AquaCORTEX — designed for technical evaluators, investors, and implementation partners.

MODULE 1

AquaSense

"The Sensory Nervous System of Your Aquaculture Operation"

Problem Addressed

Fragmented, delayed, or non-existent water quality and environmental monitoring leading to reactive crisis management rather than proactive optimization.

Key Capabilities

  • • Continuous IoT telemetry from multi-parameter sensors
  • • Water quality monitoring (DO, pH, temp, salinity, ammonia)
  • • Environmental condition tracking (weather, tides, currents)
  • • Anomaly detection with threshold-based alerting
  • • Historical trend analysis and pattern recognition
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Integration Points

Feeds data into BioTwin for growth modeling and FeedBrain for feeding optimization

Real-time dashboard mockup showing sensor network topology and live data streams

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Integration Points

Receives environmental data from AquaSense, growth predictions from BioTwin, and sends execution commands to FarmGrid

Animated feeding cycle with decision logic flow and efficiency metrics

MODULE 2

FeedBrain

"Precision Nutrition, Zero Waste"

Problem Addressed

Manual feeding schedules lead to overfeeding (waste, pollution) or underfeeding (stunted growth), with no adaptation to real-time fish behavior or environmental conditions.

Key Capabilities

  • • Behavioral analysis through computer vision
  • • Environmental condition integration
  • • Autonomous feeding schedule generation
  • • Drone and smart feeder hardware integration
  • • Feed conversion ratio (FCR) optimization
MODULE 3

BioTwin

"Your Farm's Digital Mirror"

Problem Addressed

Uncertainty in growth trajectories, harvest timing, and biomass estimation leading to supply chain misalignment and financial planning challenges.

Key Capabilities

  • • Individual cohort digital twin modeling
  • • Growth rate prediction based on feed, environment, genetics
  • • Biomass estimation with confidence intervals
  • • Harvest timing optimization
  • • Scenario simulation (what-if analysis)
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Integration Points

Consumes data from AquaSense and FeedBrain, provides forecasts to FarmGrid and investor dashboards

Interactive digital twin visualization with growth curve projections

🗺️

Integration Points

Aggregates insights from all modules, serves as primary human interface layer

Multi-screen dashboard layout showing farm fleet overview, individual farm detail, and alert management

MODULE 4

FarmGrid

"Command Center for Your Aquaculture Empire"

Problem Addressed

Multi-site operations lack unified visibility, leading to inconsistent management practices and delayed response to critical events.

Key Capabilities

  • • Multi-farm fleet dashboard with health scoring
  • • Hatchery coordination and seed tracking
  • • Alert management and escalation workflows
  • • Team task assignment and completion tracking
  • • Mobile field operations support
MODULE 5

EdgeAI Nodes

"Intelligence at the Water's Edge"

Problem Addressed

Remote farm locations with unreliable connectivity cannot depend solely on cloud processing, requiring local autonomous decision-making.

Key Capabilities

  • • On-site AI processing for time-critical decisions
  • • Offline operation capability with cloud sync
  • • Local data buffering and compression
  • • Edge-based computer vision processing
  • • Reduced latency for autonomous feeding
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Integration Points

Bidirectional sync with Cloud Neural Core, local command execution for FeedBrain and AquaSense

Network topology diagram showing edge-cloud hybrid architecture with data flow patterns

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Integration Points

Receives telemetry from all edge nodes, distributes improved models and recommendations back to FarmGrid

Neural network visualization showing learning feedback loops

MODULE 6

Cloud Neural Core

"The Collective Intelligence Engine"

Problem Addressed

Isolated farm knowledge prevents industry-wide learning and optimization improvements that benefit the entire network.

Key Capabilities

  • • Learner Module: Continuous model training from aggregated data
  • • Recommender Module: Best practice suggestions
  • • Pattern discovery and anomaly correlation
  • • Global benchmarking against regional cohorts
  • • Model distribution to edge nodes

System Architecture Overview

Edge-Cloud Hybrid Intelligence Architecture

Edge Layer

Farm Sites

  • • EdgeAI Nodes
  • • AquaSense IoT
  • • FeedBrain Automation

Cloud Layer

Neural Core

  • • Learner Module
  • • Recommender Module
  • • Analytics Engine

Interface Layer

User Access

  • • FarmGrid Dashboard
  • • Mobile App
  • • Investor Portal

Data flows from edge sensors through local processing, syncs to cloud intelligence, and delivers insights back to user interfaces

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