About
FeedRight turns sensor streams, underwater video, and farm outcomes into safer feeding decisions for modern aquaculture teams.
Our mission
Help farm teams feed only when fish, water, weather, and safety checks agree.
Live decision
Medium feed
Recommended only after oxygen, appetite, wind, and recent feeding history clear the guardrails.
44
signals checked
6
feeding actions
30m
decision cycle
Our story
Stage 01 - before deployment
FeedRight starts in simulation, where the model practices over 100,000 feeding decisions before a farm relies on it.
That baseline encodes practical rules from operators: when to pause, when to test appetite, and when water conditions make feeding unsafe.
Simulator
Stage 02 - daily production
During operations, FeedRight combines water quality, underwater cameras, weather, feeding history, growth patterns, and waste signals.
The system chooses from no feed, small, light, medium, heavy, or maximum feed while keeping every decision visible to operators.
Cage B snapshot
08:00Stage 03 - always guarded
Rules block feeding when oxygen is too low, water is outside safe temperature limits, winds are strong, or fish have been fed too recently.
The model can recommend an action, but the safety layer decides whether the feeder is allowed to run.
Guardrails
Stage 04 - continuous learning
Every cycle records the conditions, the action, and the result, including consumption, waste, oxygen impact, and fish behavior.
After 1,000-2,000 new cycles, FeedRight can retrain on the farm's own history and deploy the better model only after validation.
Operating principle
Feeding automation only works when the farm team can trust the recommendation, understand the reason, and step in when local context changes.
The right model is not the one that feeds the most. It is the one that protects growth, water quality, and operator control at the same time.
FeedRight is built at the intersection of model systems, farm operations, computer vision, field integration, and customer engineering.
Model systems
Reinforcement learning, validation, and model release workflows for feeding decisions that operators can inspect.
Site practice
Aquaculture workflows, feeding routines, safety thresholds, and calibration details shaped around working farm teams.
Field signals
Camera, oxygen, temperature, salinity, weather, and feeder data brought together as one operating view.
Human review
Operators can approve, override, and audit feeding decisions.
Private deployment
Farm data remains controlled through secure cloud and private deployment options.
Calibration first
Sensors, cameras, gateways, and feeders are checked before automation goes live.
Safety limits
Environmental and feeding-frequency limits remain active before every action.
Measured outcomes
Consumption, waste, water impact, and behavior become the learning record.
Model rollback
New models deploy only when validation beats the current production model.