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Neural Acres
Technology

Kryleos Atmos-OS —
Biological Control Intelligence

The central nervous system of every Neural Acres facility. Not a timer. Not a thermostat. A continuously learning biological control intelligence that eliminates the parameter drift responsible for yield loss across the industry.

How It Works

Sense. Think. Act.

01

Sense

Environmental and visual signals are collected from sealed cultivation zones and converted into a live biological state picture.

MicroclimateVisionTelemetryBatch Data
02

Think

Kryleos Atmos-OS interprets sensor drift, visual health signals, and batch context to decide when intervention is needed.

Edge AIAnomaly ReviewCrop StateDecision Layer
03

Act

Validated control actions adjust the facility environment, log intervention history, and prepare the training base for Phase 2 ML autonomy.

Climate ControlAudit TrailIntervention LogicPhase 2 ML
System Preview

Kryleos Atmos-OS in Real Time

The dashboard preview shows the operator-facing layer of Kryleos Atmos-OS without exposing the underlying control stack. Phase 0 focuses on monitored autonomy; Phase 2 extends Kryleos Atmos-OS into machine-learning control of the full cultivation system.

Phase 0 Role
Monitor + assist
Validate sensing, vision, dashboard, and intervention logic
Phase 1 Role
Assisted control
AI recommendations reviewed and approved by engineer before actuation
Phase 2 Role
ML full control
Closed-loop learning across climate, timing, and production actions
Control Scope
Whole facility
Climate, visual health, batch history, and operator escalation
Disclosure Level
Investor-safe
Capability proof without public replication details
Kryleos Atmos-OS · Build Dashboard--:--:--
Temperature
18.3°C
Humidity RH
89.7%
Air Quality
738 idx
Vision Score
94.2%
14:32:07Humidity correction queued
14:31:44Vision review: crop health nominal ✓
14:30:02Air exchange within safe range — no action
14:29:55Climate band stable ✓
All Systems NominalCycle Day 23 / 65
Public Architecture

Built for Industrial Scale

This public overview explains what Kryleos Atmos-OS does without exposing the vendor stack, sensor bill of materials, protocols, thresholds, or private control logic.

AI

AI & Computer Vision

  • Crop Health IntelligenceComputer vision evaluates crop state, growth quality, and contamination risk across 11 agronomic audit points.
  • Edge Decision LayerCritical control decisions run locally inside the facility.
  • Operations Advisory LayerAI-powered contextual guidance for operators across cultivation, maintenance, and compliance domains — accessible in natural language.
  • Phase 2 ML ControlMachine learning expands from monitoring to full closed-loop facility control.
  • Adaptive OptimizationCycle data trains the system to reduce drift and improve repeatability.
IO

Sensing & Control

  • Multi-Variable Sensing200+ environmental sensors monitor sealed grow zones at 5-second intervals — temperature, humidity, CO₂, and more.
  • Industrial Control LayerFacility equipment is governed through hardened automation interfaces.
  • Visual Monitoring108 dedicated vision nodes deliver continuous image streams for growth-stage tracking and contamination detection.
  • Redundant TelemetrySensor data is cross-checked before control actions are issued.
CL

Climate Control

  • Precision MicroclimateTemperature, humidity, air exchange, and light are managed as one control system.
  • Independent ZonesProduction zones can be operated separately for resilience and staggered harvests.
  • Automated InterventionsKryleos Atmos-OS corrects drift before crop quality is affected.
  • Phase 2 AutonomyML models will coordinate climate equipment directly across full production cycles.
QA

Data & Compliance

  • Batch TraceabilityEvery grow cycle produces structured records for audit and quality review.
  • Controlled Data RetentionProduction data is stored with redundancy and restricted access.
  • Compliance-Ready LogsEnvironmental and intervention history supports future GMP and food-safety audits.
  • Cycle IntelligenceHistorical data becomes the training base for Phase 2 ML control.
OP

Monitoring & Alerts

  • Operator DashboardFacility health, crop status, and intervention history are visible in one interface.
  • Escalation LogicMaintenance alerts are prioritized by biological risk and facility impact.
  • Anomaly ReviewOperators can inspect deviations without exposing raw control logic.
  • Investor PreviewThe demo dashboard communicates system behavior without disclosing implementation secrets.
IP

Kryleos Atmos-OS Core IP

  • Private Control EngineThe core control library is proprietary and not exposed publicly.
  • Biological State ModelKryleos Atmos-OS converts sensor and vision data into cultivation state decisions.
  • Decision GovernanceControl actions are constrained by validated biological operating envelopes.
  • Licensable PlatformThe same engine can later govern other fungi, botanicals, and aquaculture systems.
IP
Proprietary IP Protected: Kryleos Atmos-OS is maintained as private control IP. Public materials describe capability, not source structure, hardware recipes, thresholds, or replication-level implementation details.

See the Full Roadmap & Vision

Kryleos Atmos-OS starts with Cordyceps and scales to fungi, botanicals, and aquaculture — a universal bio-manufacturing platform.