The Moseley Platform

One platform for biological intelligence in fresh produce.

Moseley senses ethylene and the biological state of fresh produce, predicts where ripening is heading, and actively remediates to protect quality and shelf life — one closed loop across cool rooms, distribution centres, containers and trailers. Detect. Predict. Remediate. Orchestrate.

Abstract visualisation of ethylene sensing and prediction — warm orange-red light meeting rose-pink mist with cool cyan particle filaments drifting through deep space

Moseley is a biological intelligence platform that detects, predicts and remediates ethylene-driven spoilage across the fresh produce supply chain.

How it works

A continuous sense → predict → act loop

Every Moseley deployment runs the same four-stage pipeline — Detect, Predict, Remediate, Orchestrate. The architecture is signal-agnostic, but it is being commercialised first around ethylene in fresh produce.

The closed sense-predict-act loop — a sensor chip emitting particles into a glowing pink-and-cyan ring that returns as an intervention pulse on the surface below
Stage 01 / 04Biological Sensing

Detect — ethylene sensing at trace concentration

Moseley sensing begins with biomimetic receptor technology. Our sensor arrays are inspired by the way living tissue detects ethylene — selective binding chemistry that responds to ethylene at parts-per-billion levels, in the presence of confounding background gases, humidity and temperature swings. Arrays combine multiple recognition chemistries with environmental intelligence so the platform reads a biological fingerprint, not a single channel.

Biomimetic arraysppb sensitivityCross-selectivityE-Array · E-Nose
Stage 02 / 04Predictive Models

Predict — where the environment is heading

Raw signal becomes foresight. AI models and digital twins trained on Moseley sensor data turn the biological fingerprint into a ripening trajectory: how fast ethylene is accumulating and when produce will tip into over-ripeness. Prediction is what turns monitoring into intervention — you act before the damage, not after.

ML forecastingDegradation curvesEarly-warning thresholds
Stage 03 / 04Active Intervention

Remediate — act on the biological environment

Detection and prediction close the loop only when the platform can act. Moseley pairs sensing with active intervention — MOF-based remediation that removes ethylene, and signalling to existing infrastructure (HVAC, dosing, ventilation). In fresh produce this means holding ethylene below the ripening threshold to protect quality and shelf life.

MOF remediationEthylene removalActuation signalling
Stage 04 / 04Fleet Intelligence

Orchestrate — across a fleet of environments

A single room is a node. The platform coordinates many of them — cool rooms, distribution centres, refrigerated containers and trailers — into one fleet view. Cloud intelligence and fleet learning share models across sites, rank where to intervene first, and turn thousands of ethylene readings into supply-chain-level decisions.

Fleet dashboardCross-site modelsCold-chain telemetry
Where it applies

Ethylene intelligence across the fresh produce supply chain

Ethylene intelligence across the fresh produce cold chain — cool rooms, distribution centres, refrigerated containers and trailers joined by a single line of cyan light

Fresh produce cold chain

Ethylene is the hormone that ripens — and over-ripens — fruit and vegetables. Moseley detects it at trace levels, predicts the ripening trajectory and remediates it across cool rooms, distribution centres, containers and trailers. Explore fresh produce →

Signal-agnostic by design

The Detect → Predict → Remediate → Orchestrate framework is signal-agnostic. Ethylene is the first biological signal being commercialised because it offers the greatest opportunity to reduce waste and improve quality at scale. Learn about Moseley →

See the platform applied to your environment.

From farm to retail, the ethylene intelligence loop protects fresh produce at every step. Tell us what you are trying to protect.

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