The Intelligence Behind the Scent
Nosy isn’t just a sensor. It is a self-improving intelligence system for the world of smell.
At the heart of our breakthrough platform are two interwoven technologies: the Large Essence Model (LEM) and Proof of Emergent Measurements (PoEM).
Together, they form the engines of our decentralized scent intelligence network.
LEM: The Large Essence Model
Just as Large Language Models (LLMs) like GPT understand human text, the Large Essence Model (LEM) is trained to understand scent. Using a transformer-based AI architecture and federated learning, LEM translates the complex signatures of volatile compounds into meaningful insights.
How LEM Works:
Molecular to Meaningful: This process turns raw molecular signals into actionable classifications, such as identifying spoiled food, airborne toxins, or a specific wine varietal.
Adaptive & Private: Learns from millions of real-world data points without ever storing personal information, using privacy-preserving federated learning.
Always Evolving: LEM constantly adapts to new compounds and environmental changes via decentralized, verified updates.
Distributed Inference: LEM runs across a global network of registered nodes, delivering low-latency, high-accuracy scent classification at the edge or in the cloud.
PoEM: Proof of Emergent Measurements
LEM’s intelligence depends on trustworthy data. That’s where PoEM comes in. PoEM is a decentralized, cryptographically secured validation system that filters, clusters, and rewards high-quality scent data in real time.
What Makes PoEM Special:
Statistical Trust Layer: This layer uses clustering and anomaly detection to confirm the reliability of scent data from thousands of contributors.
Smart Incentives: Validated contributions earn $Nosy tokens. Outliers and fraudulent data are penalized automatically.
Transparent Missions: Community-defined scent missions let users collect data with a purpose, like monitoring air quality, profiling wine aromas, or detecting narcotics.
On-Chain Auditability: Every data submission, validation, and reward is recorded on-chain for complete transparency and traceability.
A closed feedback loop
Devices capture volatile compound data from the physical world.
PoEM validates and filters this data in real-time across a decentralized network.
LEM trains on this validated data, improving its scent classification abilities.
New insights flow back to users and devices via decentralized inference.
This architecture transforms scent from a forgotten sense into a continuously evolving layer of machine-readable meaning, unlocking vast potential for safety, health, commerce, and creativity.