An Operating System
For The Physical World.

Meshcore AI transforms cameras, sensors, robots, and facilities into a coordinated real-time intelligence network — orchestrating operations across the world's most complex environments.

View System Demo
SOC 2 TYPE IION-PREM READYEDGE INFERENCESUB-50MS LATENCY
meshcore://operations/atlas-01
SYNCEDv2.41.0
Live Events
14:02:11
Conveyor B-7 anomaly detected
14:02:04
AGV-204 rerouted via aisle 3
14:01:58
Dock 12 readiness confirmed
14:01:43
Camera cluster N-2 calibrated
14:01:21
Forklift-08 idle threshold
14:00:59
Shift handoff verified
Core
Dock A
Dock B
Robotics
Sorting
Inbound
Outbound
Camera N-2
Sensor S-7
ANOMALY · CONVEYOR B-7
predicted failure in 14 min
FACILITY · ATLAS-01
42.3601° N · 71.0589° W
Telemetry+ 12.4%
Throughput2,481/h
Latency38ms
Active assets1,204
AI Recommendations
AIShift 3 staff to zone B to absorb inbound surge
AIPre-cool reefer dock 4 — ETA truck 12 min
AISchedule maintenance: robot R-118
Sites under management
640+
Real-time decisions / day
24.1M
Avg. inference latency
38ms
Operator hours saved / mo
1.2M
Industries
Scroll to explore ↓
Operators · Worldwide

Trusted by operators managing large-scale physical infrastructure.

MERIDIAN LOGISTICS
Logistics
AXIOM MANUFACTURING
Manufacturing
NORTHGRID INFRA
Infrastructure
VESTA HEALTH SYSTEMS
Healthcare
AERO CONTINENTAL
Aviation
ORBIT FREIGHT
Logistics
How It Works

From sensor to coordinated action — in milliseconds.

01
CORECamerasSensorsRobotsDronesERP/WMSPLCs

Connect

Integrate cameras, IoT devices, robots, drones, ERP/WMS systems, and industrial sensors through a unified protocol layer.

02
$ meshcore semantic --inspect
entity forklift_08
type: AGV · zone: B-2 · load: 412kg
entity dock_12
state: ready · eta: 00:11:42
relation forklift_08 → dock_12
↳ optimal path computed (3.2s)
↳ updating world model_

Understand

Meshcore AI builds a live semantic model of facilities, assets, and operations — a digital twin that reasons in real time.

03
HUMANROBOTSYSTEM

Coordinate

The platform predicts disruptions and orchestrates workflows across humans, robots, and systems autonomously.

Core Capabilities

One platform. Every layer of physical operations.

Real-Time Facility Intelligence

Continuous semantic awareness across every camera, sensor, and asset.

Autonomous Incident Detection

Detect anomalies, safety events, and operational drift the instant they occur.

Predictive Operational Analytics

Forecast bottlenecks, equipment failure, and demand surges before they happen.

Cross-System Coordination

Orchestrate ERP, WMS, MES, and robotics from a single decision layer.

Digital Twin Infrastructure

A live, queryable 3D model of every facility — synchronized to physical state.

Human + Robot Workflow Optimization

Coordinate teams and autonomous fleets through unified task assignment.

Edge AI Processing

On-device inference with sub-50ms latency. Resilient to network loss.

Multi-Site Operational Visibility

A single command surface across thousands of facilities and time zones.

Live Operations Center

Mission-control for the physical world.

A single cinematic surface for every event, asset, and decision across your operations.

OPERATIONAL|REGION: NA-EAST · 14 sites · 4,812 assets
Live1H6H24H
Overview
Assets
Alerts
Workflows
Heatmaps
Sensors
Robots
Reports
Sites
ATLAS-01
NIMBUS-04
KITE-12
ORION-22
ATLAS-01 · Floor density × throughputdensity
Operational Timelinelast 60 min
Inbound
Robotics
Outbound
Anomaly Alerts3 Active
ANM-304100:14
Vibration spike — robot R-118
ATLAS-01 · Zone B
ANM-303700:42
Temperature drift — reefer dock 4
NIMBUS-04
ANM-303201:06
Conveyor B-7 micro-stoppage pattern
KITE-12 · Sort line
AI Recommendations
Reroute AGV-204 via aisle 3 — saves 11 min
Open dock 7 early — inbound surge detected
Reassign team B to outbound — 92% confidence
Sensor Network
Enterprise Security

Engineered for the operators of critical infrastructure.

Security and sovereignty are not features — they are the foundation. Every byte of data and every inference path is designed for the regulated enterprise.

ISOLATED
On-Prem Deployment
Run entirely within your network perimeter. Air-gapped configurations supported.
LOCAL
Edge Inference
AI runs on local hardware. No video or telemetry leaves the facility.
REALTIME
Sub-50ms Latency
Hot-path decisions complete on-edge. Cloud sync is asynchronous.
AUDITED
SOC 2 Type II Ready
Continuous controls, audit logging, and least-privilege access by default.
ZTNA
Zero-Trust Architecture
mTLS between every component. Per-tenant key isolation and rotation.
SCALE
Linear Scale
Horizontal scaling from a single facility to thousands of sites.
Reference ArchitectureHEALTHY
EDGE · FACILITYCamerasSensorsRobotsDronesMESHCORE EDGE · INFERENCE + SEMANTIC MODELmTLS · ZTNACONTROL PLANE · POLICY · OBSERVABILITYCLOUD · TENANT-ISOLATEDSOC2HIPAAISO 27001
P95 Latency
38ms
Uptime SLA
99.99%
Audit Events
2.4M/d
Deployment

From signed to live in 4 weeks.

Week 1Complete

Infrastructure Mapping

Site survey, network audit, and asset inventory across all facilities.

Week 2Complete

Sensor + System Integration

Connect cameras, IoT devices, robots, and ERP/WMS systems.

Week 3In Progress

AI Operational Modeling

Train semantic model and calibrate workflows on live operational data.

Week 4Queued

Live Deployment

Cutover to production with continuous monitoring and SLA support.

Average TTL
28 days
Sites integrated
640+
On-time delivery
100%

Bring intelligence to every physical operation.

Deploy across warehouses, factories, and infrastructure environments in weeks.