DNotifier provides the infrastructure for building autonomous inspection agents, safety agents, security agents, maintenance agents, emergency response agents and multi-agent physical operations.
A Physical AI agent is more than an LLM prompt. It needs perception, event subscriptions, operational context, memory, tools, policies and controlled actions.
Receive information from cameras, drones, sensors, robots, SCADA, IoT systems and human operators.
Convert raw signals into structured operational events and determine which agents should respond.
Retrieve asset information, historical inspections, maintenance records, previous incidents, spatial information and operational knowledge.
Evaluate evidence, determine confidence, identify risk and decide what information or action is required next.
Allow agents to interact with drones, cameras, enterprise APIs, GIS systems, maintenance platforms and operational services.
Apply policies, confidence thresholds, permissions, human approvals and safety constraints before an operational action is executed.
Execute a digital or physical action and verify whether the action achieved the intended result.
DNotifier can provide reusable agent patterns that organizations can customize for their assets, operating procedures, data and industry requirements.
Determines whether an asset requires inspection, coordinates evidence collection and produces structured inspection findings.
Correlates gas readings, imagery, thermal data, OGI observations and historical asset context.
Detects and evaluates operational safety events and coordinates appropriate response workflows.
Combines cameras, drone patrols, access events and AI findings into a unified security workflow.
Uses asset condition, inspection history, telemetry and maintenance records to identify maintenance requirements.
Coordinates multiple agents and operational systems during high-priority incidents.
Monitors environmental signals and identifies abnormal conditions requiring investigation.
Continuously evaluates asset condition using multiple signals and updates operational risk.
Converts operational requirements into missions for drones, robots or other autonomous systems.
A DNotifier agent follows a structured lifecycle instead of simply generating a response from a prompt.
Complex physical operations rarely belong to a single AI agent. DNotifier allows specialized agents to collaborate through events and shared operational context.
Determines what needs to be inspected and what evidence should be collected.
Determines which drone or autonomous device should execute the inspection.
Analyzes images, video, thermal information or other visual evidence.
Combines findings with asset history and operational policy to determine risk.
Coordinates notifications, approvals, work orders and downstream systems.
Coordinates the entire response when the situation crosses an operational threshold.
A physical asset cannot be understood from one image or one sensor reading. Agents need historical context to determine whether a condition is new, recurring, improving or getting worse.
Reasoning alone cannot inspect a pipeline, launch a drone or create a maintenance work order. Physical AI agents need controlled tools that connect intelligence to real-world operations.
DNotifier treats drones, robots and other connected systems as operational actuators that agents can interact with through governed tools and workflows.
Additional physical evidence is required.
Select an available drone and appropriate payload.
Submit and monitor the inspection mission.
Analyze the evidence collected during the mission.
Determine whether the finding requires escalation.
Notify operators and update the relevant business system.
Enterprise Physical AI requires governance. DNotifier allows organizations to define where AI can act independently and where human authorization is required.
AI detects an event and provides operators with evidence and recommendations.
AI prepares the action while an operator decides whether it should execute.
AI requests authorization before executing a defined operational action.
Approved low-risk actions execute automatically under configured policies.
The platform confirms whether the action produced the expected operational result.
Low-confidence or high-risk situations are escalated to authorized personnel.
Oil and gas environments combine large physical assets, remote infrastructure, industrial control systems, cameras, sensors and strict operational procedures. A multi-agent architecture can connect these systems into intelligent workflows.
Monitor pipeline events, inspections, anomalies and risk conditions.
Coordinate tank inspections and analyze visual evidence for anomalies.
Analyze visual and operational signals associated with flare systems.
Correlate gas-related events with drone, camera and sensor evidence.
Coordinate safety findings, alerts, escalation and incident workflows.
Coordinate autonomous patrols and correlate security events across cameras and drones.
Coordinate incident investigation and response across multiple agents and operational systems.
Continuously evaluate the changing risk profile of critical infrastructure.
Complex operational incidents can be decomposed into smaller responsibilities handled by specialized agents.
DNotifier provides the infrastructure layer so developers can focus on agent intelligence instead of rebuilding event transport, device connectivity, memory, workflows and operational integrations.
Start an agent when a relevant operational event occurs.
Give the agent the asset and operational information it needs to reason.
Allow the agent to interact with authorized operational systems.
Publish structured results so another agent or workflow can continue the process.
Answers to common questions developers, engineering teams and enterprises ask when evaluating Physical AI, autonomous agents and intelligent drone operations.
Connect events, devices, asset intelligence, AI reasoning and autonomous workflows through one Physical AI infrastructure layer.