TwinEdge Agentic Analytics
Industrial DataOps plus physics-grounded agents, recommendations, traces, and replay.
Agentic Analytics turns physics-aware operational context into explainable recommendations. Agents can observe, explain, draft, validate, diff, and route approval while preserving evidence for replay.
TwinEdge vs generic AI
Agentic analytics built for industrial decisions, not open-ended chat.
Traditional dashboards and generic copilots stop at summaries. TwinEdge starts from DataOps context, physics model outputs, and operating envelopes, then uses bounded agents to validate recommendations, route approvals, and keep replay evidence before work reaches operations.
Observe
Explain
Draft
Validate
Approve
Replay
Agentic, but governed for industrial operations.
Platform in action
Agentic recommendations are grounded in governed physics context
Agentic recommendations are grounded in trusted data context, physics model outputs, approval workflows, source evidence, and replayable operational records.
AI DataOps workspace
Agents explain and draft from bounded DataOps context instead of open-ended source-system access.
Agent Hub impact board
The Agent Hub shows governed agent value, live activity, approvals, and impact across Sentinel, Diagnostics, Planner, Capital Economist, Inventory, Compliance, and Knowledge agents.
Inventory Intelligence agent
Inventory Intelligence surfaces active items, stockout risk, pending reorders, savings, forecasts, and approval-ready reorder recommendations.
Standards and profile registry
Profile adapters over the canonical graph show visible states such as preview and planned, with validation endpoints and read-only profile registry paths.
PM recommendations from O&M manuals
AI-extracted PM recommendations carry source document, cadence, priority, confidence, physics context, and approval state before becoming schedules.
Workflow
From signal context to approved recommendation
Connect industrial sources, build trusted context, govern recommendations, and turn approved decisions into operational work.
Observe with operational context
Agents read bounded context from source metadata, asset models, physics outputs, canonical graph, quality checks, and recent telemetry.
Draft plans, not uncontrolled actions
The agent prepares findings, recommendations, work drafts, and diffs before any operational team approves the action.
Replay what happened
TwinEdge keeps source context, reasoning trace, dry-run output, approvals, and final action evidence for review.
Capabilities
Agentic Analytics capabilities
Recommendation workspace
Evidence-backed findings with source links, asset context, physics signals, risk, and suggested actions.
Approval gates
Dry-run plans, diffs, eval gates, and human review before routing work or publishing products.
AI data products
REST and MCP surfaces expose governed operational context to trusted apps and AI clients.
Engineering controls
Engineering controls for industrial AI.
TwinEdge can show real telemetry, local inference, protocol flows, and agent traces without claiming uncontrolled autonomy or SCADA replacement.
Read-only first
Physical writeback is disabled by default and recommendations pass through approval gates.
Replayable evidence
Plans, diffs, source context, and approval history remain available for review.
Deployment choice
Cloud-connected, local, and offline paths support evaluation without forcing one architecture.
Source system respect
TwinEdge works above SCADA, historians, CMMS, GIS, LIMS, ERP, and data lakes rather than pretending to replace them all.
Outcomes
Operational outcomes
Teams get the context, controls, and execution path needed to move from noisy industrial data to approved operational action.
Operators
Receive recommendations with the physics, the evidence, and the confidence boundaries.
Reliability teams
Convert recurring physics and failure signals into repeatable work patterns instead of one-off dashboard hunts.
IT and governance
Use bounded context, redaction, approval, and replay instead of open-ended copilots.
Maintenance planners
Turn approved recommendations into scoped work drafts, schedule options, parts context, and execution evidence.
Connected platform
Extend the same context across the operating layer
DataOps Workbench creates the physics-aware, AI-ready context.
Agentic Analytics uses that context to explain, draft, and validate recommendations.
TwinEdge OS supports cloud-connected, offline, and protocol-rich edge deployments.
AssetOps EAM and Field close the loop from recommendation to evidence-backed work.
Water, wastewater, chemical, water loss, lab, and facility products package industry workflows.
REST and MCP data products make context available to enterprise applications and AI systems.
Evaluate TwinEdge
Plan your first TwinEdge workflow.
Review the operating model with our team, or download TwinEdge to evaluate the platform in your own environment.