AI data products
REST and MCP products that expose governed industrial context.
TwinEdge publishes AI-ready operational context through governed REST and MCP surfaces so applications, agents, and enterprise systems can use the same validated asset model.
REST APIs
MCP tools
Catalog
Graph context
Governance
Monitor
APIs and MCP are products backed by DataOps, not raw telemetry endpoints.
API/MCP in action
Product surfaces that turn governed context into trusted access
Enterprise teams get more than another endpoint: TwinEdge packages validated source context, graph bindings, standards, contract metadata, testability, audit, and monitoring into reusable REST and MCP products.
APIs and MCP product workspace
The DataOps surface packages source context, graph bindings, standards, product metadata, and monitored publication paths before anything is exposed.
Validated profile contracts
Standards and profile adapters turn native context into bounded, documented contracts that apps and agent clients can trust.
Agent-facing context products
AI DataOps keeps agent access bounded to approved source context instead of giving models open-ended access to operational systems.
Source context before publication
Connector setup, runtime state, credentials, browsing, and audit context stay visible before a REST or MCP surface is activated.
Asset-bound product context
Model binding and mapping review connect raw industrial data to asset instances so published API and MCP results carry operational meaning.
Workflow
From validated context to trusted API and MCP products
Start with validated source context, turn it into a product contract, publish it through REST and MCP, then monitor every client and change.
Package context as a product
Start from source metadata, asset bindings, profile adapters, lineage, and canonical graph context so every product has a clear operational scope.
Expose REST and MCP surfaces
Publish bounded REST APIs and read-only MCP tools with schemas, tenant scope, catalog entries, and display-safe payloads for trusted clients.
Monitor every client and change
Track usage, audit calls, review publication changes, and keep replay evidence so apps and AI agents consume context through governed channels.
Capabilities
API/MCP product capabilities
Each surface is designed as a governed data product: documented, testable, scoped to a tenant, visible in catalog, and bounded for applications and agent clients.
Contracted product surfaces
Versioned schemas, profile-backed payloads, catalog metadata, and testable examples make the surface usable beyond one dashboard.
MCP tools for agents
Agent clients receive bounded operational context through read-only tools instead of direct source-system access.
Governance and observability
Approval state, auth boundaries, audit trails, monitor signals, and replay evidence stay attached to every product.
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
Outcomes for AI, apps, and operations
AI, application, and governance teams work from the same validated product contracts instead of separate exports, raw tag access, or one-off integrations.
AI teams
Give agents trusted industrial context with fewer prompt-time assumptions and less risk of exposing raw operational systems.
Application teams
Build apps, BI, and integrations against stable product contracts instead of ad hoc tag queries and one-off exports.
Governance teams
Review what was published, who used it, what changed, and which source evidence supports the result.
Connected platform
Where API/MCP products plug in
The API/MCP layer sits between governed DataOps context and the clients that need it: enterprise apps, AI agents, BI, field workflows, and audit surfaces.
Enterprise applications consume REST products with cataloged schemas and tenant-scoped authorization.
AI agents consume MCP tools with bounded, display-safe operational context.
DataOps Workbench owns source readiness, standards, graph bindings, and publication review.
Agentic Analytics uses the same governed products to explain, draft, validate, and replay recommendations.
AssetOps EAM, Field, GIS, BI, and external systems reuse the same validated context.
Monitor and audit surfaces show client usage, product drift, test runs, and replay evidence.
Evaluate TwinEdge
Turn one governed data product into trusted REST and MCP access.
Start with one operational source and one asset context, package it as a governed contract, and prove how applications and agents consume it without bypassing controls.