The Platform / Cortex
The AI answer layer for operational truth
Cortex gives Copilot, Claude, Gemini, and your own agents governed access to the current operational state of your enterprise, across people, devices, software, vendors, contracts, identities, and worksites.
Not another chatbot. Not another data warehouse. The current operating reality of your business, available as an answer.
Watch it happen
“We need to remove $5 million from next year’s IT budget without reducing headcount or increasing operational risk. Where should we start?”
Cortex
I found $7.2M in addressable savings.
$5.4M can be captured with low operational risk.
| Opportunity | Annual impact | Why |
|---|---|---|
| Unused and underused software | $2.1M | 3,842 paid entitlements show no meaningful activity |
| Healthy devices scheduled for refresh | $1.5M | 4,106 devices can remain in service 12–18 months |
| Overprovisioned mobile plans | $1.0M | 623 plans don’t match actual travel or usage |
| Duplicate vendor commitments | $800K | Overlapping capabilities across 3 active agreements |
Recommended sequence
$5.4M
Year-one savings
0
Employees affected
Low
Operational risk
96%
Confidence
Grounded in: Workday, ServiceNow, Intune, Coupa, SaaS telemetry, carrier billing, contracts, travel and expense data.
“What changes if headcount grows 8% and the acquisition closes in September?”
Cortex
The opportunity falls from $5.4M to $4.8M.
$4.8M
Revised savings
93%
Confidence
It didn’t just retrieve a number. It recalculated the operating plan against a future business event.
“Show me why you believe the software number.”
Cortex
The $2.1M opportunity is based on:
Every recommendation is traceable to its source record, effective date, policy, and governing contract.
Illustrative example, shown to demonstrate the reasoning model.
Cortex identifies the opportunity, explains the reasoning, and traces every fact. When action is approved, the XOPS System of Action converts the recommendation into governed outcomes across the affected systems.
Proposed governed outcomes
Why Cortex can answer what other AI cannot
1
Workday knows the employee. Intune knows the device. Coupa knows the purchase. The contract knows the commercial rights. Usage telemetry knows what actually happened.
2
The Living Knowledge Graph maintains the current operational state and relationships across them.
3
AI assistants receive the relevant context, provenance, policy, and relationships required to answer the question.
4
Approved recommendations become deterministic, auditable outcomes across existing systems.
When every system owns a slice of the truth, every operational question is normally a forensic exercise.
Q1 · Capacity
SaaS admin: 432. Finance: 480 paid. Identity: 510 in the group. Three teams, three answers, no one can renew with confidence.
Q2 · Access
Okta, ServiceNow, every SaaS app, the badge system. Five surfaces. Nobody’s queried them together since the last audit, which took a quarter.
Q3 · Risk
HR ended Friday. Identity disabled Monday. Three SaaS apps still active a week later. The forensic answer takes a quarter and a subpoena.
Cortex makes every one of these a single query against your operational truth, resolved in seconds, against the same reality every other system is writing to.
How it works
Your systems of record
Workday · Okta · ServiceNow · Intune · Tanium · Coupa
Living Knowledge Graph
Operational Truth
Reached via Cortex (MCP)
Continuously reconciled
One coordinated view
Cortex exposes the current operational state maintained by XOPS, with every fact grounded in its source systems and relationships.
Reality, not reporting
Real-time sync, not nightly ETL. The graph reflects operational state as it actually is: entities, relationships, events as they change.
Continuously reconciled
Cortex exposes the current operational state maintained by XOPS, with every fact grounded in its source systems and relationships.
One governed AI interface
Copilot, Claude, Gemini, custom agents, and conversational experiences access the same operational truth through governed MCP access.
The same question, the same answer, every surface
Microsoft Copilot
“Where can we cut $5M without adding risk?”
$5.4M identified, 96% confidence
Claude
“Where can we cut $5M without adding risk?”
$5.4M identified, 96% confidence
Internal executive assistant
“Where can we cut $5M without adding risk?”
$5.4M identified, 96% confidence
Three more impossible questions
Restructuring
“If we separate this business unit, what must move, what must be duplicated, and what will the TSA cost?”
$4.2M estimated TSA cost, 18 months.
340 systems require duplication before day one.
Pulls from Workday, ServiceNow CMDB, Coupa, identity systems, and contract terms.
Vendor strategy
“Which recommendation should we execute first before the Microsoft renewal, and why?”
Fix the relationship gap first.
Every discount and term negotiation depends on having an owned relationship in the room.
Pulls from Coupa, contract terms, usage telemetry, and relationship ownership records.
Operational risk
“Which employees would be unable to work Monday morning if this identity migration happened tonight?”
1,184 employees are at risk.
73% of the exposure traces to three unresolved identity dependencies. Estimated correction time: 14 hours.
Pulls from Okta, Workday, ServiceNow CMDB, and device management.
Illustrative examples, shown to demonstrate the reasoning model.
| Typical enterprise AI | Cortex |
|---|---|
| Searches indexed content | Reasons across current entities and relationships |
| Returns what a source says | Reconciles where sources disagree |
| Treats each prompt independently | Maintains enterprise context and Position |
| Produces an answer | Produces evidence, assumptions, and next actions |
| Stops at recommendation | Hands approved decisions to the System of Action |
Ask a cross-enterprise question using your own systems, policies, and operating context.
