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Aquabyte Tech Stack

AI-powered camera systems for real-time fish farm monitoring and decision-making

Technology, Information and Internet Laksevåg, Vestland 51–200 employees Founded 2017 Privately Held

Aquabyte deploys camera-based monitoring systems into fish farms, using PyTorch and edge ML (NVIDIA Jetson) to extract health and behavioral metrics from live video feeds. The tech stack—Python, Snowflake, dbt, Apache Airflow—suggests a data pipeline designed to ingest and transform high-volume visual data into actionable insights. Active projects span 3D scene reconstruction, weight estimation, feeding plan automation, and hardware observability, indicating Aquabyte is scaling from basic fish counting toward predictive farm operations.

Tech Stack 16 technologies

Core StackLinux Python Snowflake dbt Apache Airflow pandas Docker AWS PyTorch Go ARM64 SQL NVIDIA Jetson WebRTC FFmpeg GStreamer

What Aquabyte Is Building

◆Challenges

Understanding fish populations
Feeding plan optimization
Limited bandwidth connectivity
Remote hardware reliability
Remote deployment process
Hardware failure detection
Improving fish farm efficiency
Quantifying fish weights
Detecting fish health
Generating feeding plans

▲Active Projects

3d scene reconstruction pipelines
Image and video inference pipelines for fish weight and health estimation
Next-generation sensing capabilities
Real-time feeding plan generation
Edge deployment of ml algorithms
Deployment of aquaculture monitoring systems
Data collection and curation
Remote deployment process improvement
Burn-in testing automation
Observability instrumentation for hardware failure detection

Hiring Activity

Accelerating10 roles · 5 in 30d

Department

Engineering

9

Product

2

Seniority

Mid

10

Senior

1

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About Aquabyte

Aquabyte builds hardware-embedded AI systems for aquaculture operators, ranging from open-pen sea farms to closed land-based facilities. The platform captures video from underwater cameras, runs inference to measure fish weight, growth, lice load, and behavioral markers (swim speed, tilt, breathing patterns), then surfaces findings through a data pipeline to farmers' dashboards. Core use cases include early disease detection, lice outbreak prevention, and feed optimization. The company operates across Norway, the US, and Chile, with an engineering-focused team concentrated on edge deployment and data infrastructure.

HeadquartersLaksevåg, Vestland

Company Size51–200 employees

Founded2017

Hiring MarketsUnited States, Chile, Norway

Frequently Asked Questions

What technology does Aquabyte use to monitor fish farms?

Camera-based visual monitoring with edge ML inference (PyTorch on NVIDIA Jetson), backed by cloud pipelines (Snowflake, dbt, Apache Airflow) for data aggregation and decision support.

What metrics does Aquabyte measure in fish farms?

Fish weight, growth rates, skin health, fin damage, sea lice counts, swim speed, swim tilt, and breathing index—used to detect health issues and optimize feeding and treatment timing.

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