AI Workflows

Intelligent Automation Powered by LLMs

Miradoris integrates large language models directly into the workflow engine. Create automation rules in natural language, deploy AI-powered decision nodes, and let the platform generate context-aware responses to operational events — all governed by your existing policies.

Natural language

Workflow creation

<2s

Decision latency

Policy-governed

Every AI action

Workflow Creation

Describe intent, deploy automation

Instead of assembling workflow nodes manually, describe what you want in plain language. The platform parses your intent, identifies the relevant entities and triggers, builds the workflow graph, and presents it for review. Approved workflows deploy immediately.

Natural language to workflow graph compilation

Describe your intent in plain text and the engine builds the executable graph.

Automatic entity and trigger resolution

Assets, zones, and conditions are identified and linked from your description.

Human-in-the-loop review before deployment

Generated workflows require explicit approval before going live.

Version control and rollback for generated workflows

Every generated version is tracked with full diff and one-click rollback.

Natural Language Input

Operator

When any drone detects a temperature anomaly above 85C in Zone B, pause nearby operations and dispatch an inspection team

Generated Workflow

4 nodes compiled from natural language input

1

Monitor temp

2

Detect anomaly

3

Pause ops

4

Dispatch team

Ready for review and deployment

LLM Decision Flow

LLM Decision Nodes

AI-powered branching within operational workflows

Traditional workflows branch on simple conditions. Miradoris introduces LLM decision nodes that can reason over unstructured data — inspection photos, maintenance logs, sensor narratives — and route the workflow accordingly. Every decision is logged with its reasoning chain for auditability.

Unstructured data reasoning (images, text, logs)

LLM nodes interpret inspection photos, maintenance narratives, and sensor logs.

Configurable confidence thresholds for routing

Set minimum confidence levels that determine automatic vs. manual routing.

Full reasoning chain logged per decision

Every AI decision includes a complete audit trail of its reasoning steps.

Fallback to human review below confidence threshold

Uncertain decisions are automatically escalated to qualified reviewers.

Anomaly Response

Automated response to operational anomalies

When the platform detects an anomaly — unusual sensor readings, unexpected asset behavior, or policy violations — the AI workflow engine evaluates the situation, determines the appropriate response, and initiates corrective action. Responses range from automated alerts to full remediation workflows.

Multi-signal anomaly correlation

Correlates data from multiple sensors and sources to confirm anomalies.

Severity assessment and escalation routing

AI evaluates severity and routes to the appropriate response tier.

Automated containment actions within policy bounds

Initiates corrective measures automatically without exceeding defined policy limits.

Post-incident report generation

Generates structured incident reports with timeline, actions taken, and recommendations.

Anomaly Detected

14:32:07 UTC

AI Assessment

Severity High

Source Turbine T-14 vibration

Recommended actions

Reduce turbine RPM to safe threshold Dispatch maintenance crew to T-14 Notify site supervisor on shift

Containment initiated Auto-response active

Generated Tasks

Inspect solar panel array C-12

Drone D-04

~25 min

Requires: Wind < 20 kn

Replace coolant filter on Generator G-07

Maintenance crew B

~45 min

Requires: Generator offline

Perimeter scan of Zone E boundary

Patrol unit R-11

~35 min

Requires: Daylight hours

3 tasks generated from current operational context

Context-Aware Tasks

Intelligent task generation from operational context

The AI engine generates actionable tasks by analyzing the current state of operations, pending maintenance schedules, weather forecasts, and asset availability. Generated tasks include all necessary context: location, required equipment, estimated duration, and prerequisite conditions.

Contextual task synthesis from operational state

Tasks are generated based on live conditions, not static schedules.

Prerequisite dependency resolution

Each task includes conditions that must be met before execution begins.

Estimated duration and resource allocation

AI predicts time requirements and allocates equipment automatically.

Automatic assignment to qualified personnel or assets

Tasks are routed to available, certified operators or autonomous assets.

Be among the first

We are looking for partners willing to test Miradoris in real operational environments. Early adopters get priority access to the platform at significantly reduced rates.

We'll review your request and follow up. No unsolicited contact.