Contextual Operations Model

The Operational Graph

Traditional systems manage facts in isolated database tables. OpsMind connects those facts into a living graph, enabling AI to reason across operational boundaries.

Operational Representation

Operational Graph Entity Types

OpsMind maps eight fundamental operational dimensions to model how your business actually functions.

👥

People

Employees, teams, supervisors, certified technicians, shift leads, and availability.

⚙️

Assets

Machines, vehicles, production lines, facilities, HVAC units, and sensor nodes.

📦

Inventory

Replacement components, raw materials, finished stock, safety buffer, and SKU locations.

📍

Locations

Factories, warehouses, delivery zones, service routes, and transit hubs.

📋

Work

Maintenance work orders, inspections, production batches, and service tasks.

🏷️

Commerce

Customer orders, SLAs, delivery commitments, purchase orders, and supplier contracts.

⏱️

Time

Shift schedules, maintenance windows, lead times, and SLA countdowns.

📜

History

Past incidents, MTBF metrics, historical throughput, and repair outcomes.

Why Graph Modeling Matters

Cross-System Intelligence

A CMMS knows Machine 17 has a bearing. An ERP knows there are 3 bearings in inventory. Procurement knows supplier lead time is 14 days. Production knows Machine 17 is business-critical.

OpsMind connects these facts into one operational sentence:
"Bearing inventory is projected to reach zero in 11 days, creating a critical downtime risk on Machine 17 before the 14-day supplier delivery window closes."

Graph Query Engine

Natural-language operational graph queries executed in milliseconds:

> QUERY: "Find all machines approaching service interval without reserved spare parts in Stockroom A"
✓ MATCH FOUND: Machine 12 (Line 2) & Compressor 04 (Site B)

Connect Your Operational Graph

Turn fragmented operational records into unified cross-system intelligence.