Connect Everything
Bring physical devices and operational systems into a common infrastructure layer.
- •Drones
- •Cameras
- •Robots
- •Industrial sensors
- •SCADA and PLC systems
DNotifier connects drones, cameras, robots, sensors, industrial control systems and enterprise applications through an event-driven Physical AI infrastructure layer.
Modern industrial environments already have cameras, sensors, drones, robots, SCADA systems, enterprise software and operational teams. The missing layer is intelligent orchestration between them.
Bring physical devices and operational systems into a common infrastructure layer.
Specialized AI agents interpret events using real-time telemetry, historical information, asset knowledge and operational policies.
Move beyond alerts by connecting AI decisions to operational workflows and physical actions.
DNotifier creates a common operational layer between physical devices, AI agents and enterprise systems. Signals enter the platform, become structured events, gain contextual intelligence and can ultimately trigger governed actions.
Connect cameras, drones, robots, sensors, PLCs, SCADA systems and edge services while keeping vendor-specific protocols behind reusable gateway abstractions.
Normalize telemetry, alarms, video findings, mission updates and operational signals into structured events that can be routed, correlated and consumed by agents.
Specialized agents reason over live telemetry, historical inspections, maintenance records, spatial information, policies and enterprise knowledge.
Translate AI findings into governed actions: dispatch a drone, request an inspection, notify HSE, open a work order or escalate to an authorized operator.
Push verified outcomes into ERP, CMMS, SCADA, GIS, HSE, control-room and operational systems, then continue monitoring the environment.
DNotifier provides reusable infrastructure components that organizations can combine to build AI-powered physical operations.
Convert physical-world signals into structured, routable events that can trigger agents, workflows and enterprise actions.
Coordinate specialized agents for inspection, safety, security, maintenance, environmental monitoring and emergency response.
Create a persistent intelligence layer around every physical asset and connect current state with historical operational context.
Connect autonomous systems and visual sensors to the same event-driven operational layer.
Define what happens after an AI finding, including approvals, escalation, automated actions and verification.
Give development teams the infrastructure required to build Physical AI applications without creating the entire event and orchestration layer themselves.
Physical environments continuously generate events. The event mesh creates the nervous system that lets AI agents and enterprise workflows respond to those events without tightly coupling every system together.
A gas sensor should not need to know which drone to launch, which camera to inspect, which HSE workflow to trigger or which work-order system to update.
DNotifier separates sensing from reasoning and reasoning from action.
The Emergency Response Agent can subscribe to relevant event types, retrieve asset context, evaluate policy and coordinate downstream actions without being tightly coupled to a particular sensor vendor.
Instead of building a separate integration for every device-to-application workflow, organizations can model operational conditions as events that any authorized agent or workflow can consume.
Temperature, pressure, gas, vibration, humidity, energy and other telemetry signals.
Fire, smoke, intrusion, human presence, equipment anomaly and visual inspection findings.
Mission requested, mission started, drone arrived, inspection completed, image captured and anomaly detected.
Asset risk changed, inspection due, maintenance required, abnormal condition or asset state changed.
Gas alarm, fire alarm, restricted-zone intrusion, unsafe condition and emergency escalation.
Weather alerts, emissions, environmental anomalies, water conditions and other monitoring signals.
DNotifier enables closed-loop operational workflows where AI can investigate an event, make a decision, trigger a governed action and verify the result.
A single industrial alarm can become the beginning of an autonomous investigation instead of simply becoming another notification in a control room.
A high-priority event is associated with the affected asset, location and severity.
The agent retrieves asset history, recent inspections, nearby camera context, weather and operational policy.
DNotifier selects an available drone and generates an inspection mission based on the event and asset requirements.
Visual, thermal, gas-imaging and telemetry evidence can be combined to determine whether the alarm is likely to represent a real incident.
Notify HSE, alert the control room, create a maintenance work order and continue monitoring.
The Asset Brain connects live signals with the historical context required for meaningful AI reasoning.
Instead of hard-coding drone commands into individual applications, DNotifier treats missions as part of the event-driven operational model.
DNotifier adds an intelligence and orchestration layer above individual device integrations, allowing physical systems and AI agents to operate as one connected network.
| Capability | Traditional Integration | DNotifier Physical AI |
|---|---|---|
| Device Connectivity | Point-to-point integrations | Reusable gateway and connector architecture |
| Operational Events | Vendor-specific payloads | Normalized event-driven model |
| AI Reasoning | Embedded in individual applications | Composable specialized AI agents |
| Asset Context | Distributed across multiple systems | Persistent Asset Brain |
| Automation | Scripts and static rules | AI + rules + workflows + policies |
| Human Control | Manual intervention | Configurable human-in-the-loop autonomy |
| Enterprise Integration | Custom integrations per application | Reusable APIs, connectors and event workflows |
| Closed-Loop Operations | Usually fragmented | Detect → investigate → decide → act → verify |
DNotifier is designed as an intelligence and orchestration layer rather than a replacement for the operational systems enterprises already depend on.
Connect signals from SCADA, PLCs, industrial sensors, alarms and edge systems.
Use cases: anomaly detection, alarm correlation, predictive maintenance and safety response.
Combine fixed cameras, thermal cameras, OGI systems, drone imagery and computer vision.
Use cases: leak detection, fire detection, intrusion and equipment inspection.
Push verified findings into ERP, CMMS, HSE, GIS, ticketing and control-room applications.
Use cases: work orders, incident management, compliance and asset lifecycle.
Combine physical events with asset coordinates, operational zones, maps and geographic context.
Use cases: pipeline corridors, facilities, restricted zones and emergency response.
Give operators contextual alerts instead of isolated device notifications.
Use cases: incident triage, escalation, approvals and operational supervision.
Connect models, retrieval systems, vector stores, agent services and enterprise data sources.
Use cases: AI reasoning, RAG, multimodal analysis and operational knowledge.
Not every physical action should be autonomous. DNotifier supports graduated autonomy so organizations can decide what AI may observe, recommend, request or execute.
AI detects events and provides operators with evidence, context and recommended next steps.
AI prepares an operational action while an authorized operator approves execution.
Pre-approved, low-risk actions can execute automatically under defined policies.
AI detects, investigates, acts and verifies while escalating when confidence or policy requires human intervention.
Developers can focus on operational intelligence while DNotifier provides event transport, orchestration, context, workflows and integrations.
Subscribe to asset events, retrieve operational context, analyze evidence and emit structured findings.
Connect specialized agents into an operational workflow without tightly coupling every component to a particular device.
The same infrastructure can power multiple operational applications while sharing events, AI agents, asset context and enterprise integrations.
Detect inspection requirements, dispatch drones or robots, analyze imagery and create findings.
Monitor long physical corridors using sensors, drones, cameras and AI-powered anomaly detection.
Correlate gas sensors, SCADA alarms, cameras and drone evidence to investigate potential leaks.
Detect abnormal heat, smoke and safety events and coordinate response workflows.
Combine asset history, telemetry and inspection findings to identify changing risk.
Correlate perimeter cameras, drone patrols, access events and AI findings.
Continuously monitor emissions, water, temperature, weather and other environmental signals.
Coordinate AI agents, operators, drones, sensors and enterprise workflows during incidents.
Create a continuous intelligence layer around critical physical infrastructure.
DNotifier can provide a common infrastructure layer across industries where physical assets, sensors, machines and operational teams need to work together.
Pipeline inspection, tank monitoring, leak detection, flare monitoring, security and emergency response.
Grid inspection, substation monitoring, vegetation management and infrastructure safety.
Mine-site monitoring, equipment inspection, safety intelligence and environmental monitoring.
Site progress, worker safety, equipment monitoring and automated inspection.
Perimeter security, asset monitoring, inspection and operational intelligence.
Connected infrastructure, public safety, environmental intelligence and autonomous monitoring.
Answers to common questions about Physical AI, industrial AI infrastructure and autonomous operations.
Start with one high-value operational workflow and expand into an enterprise-wide Physical AI platform.