Bring enterprise logs.
Start with operational exports from systems such as Jira, ServiceNow, Salesforce, Pega, Dynamics, Zendesk, Odoo, or similar event-heavy platforms.
THE BASICS // PROCESS MINING // DECISION TRUTH
Ghost Observatory
Observatory turns enterprise event logs into decision-grade process truth on your own infrastructure.
One engine. One question: where does work actually create value?
Most dashboards describe activity after it happened. Observatory reconstructs flow from operational data so teams can see where work waits, where effort leaks, where quality breaks, and where process claims no longer match reality.
FROM EXPORTS TO EVIDENCE
Mine the real flow.
Observatory maps cases, activities, timestamps, handoffs, waits, rework signals, throughput, quality loss, and flow behavior into auditable analytical outputs.
Use standard evidence.
Export clean tables for Power BI, Parquet, CSV, notebooks, internal reporting, or downstream automation without forcing the data into a closed SaaS workflow.
CHOOSE THE LAYER YOU NEED
Architecture
How the library is structured, why import and export stay open, what the calculation core does, and how the engine stays auditable.
Business Value
How Observatory moves beyond ticket counts and converts event logs into flow, waste, quality, capacity, and economic decision signals.
AI & ML
How intelligence stays optional and provider-neutral: bring your own model, your own jurisdiction, your own contracts, or no AI at all.
Ecosystem
Which enterprise systems can feed Observatory, which output formats it produces, and how it fits into existing analytics stacks.
Documentation
Installation, quickstart, API reference, deployment notes, metric definitions, and the practical path from export file to working analysis.
Licensing
Free use, production use, support, partnership work, warranty position, and how the commercial relationship changes by tier.
Scope
This page is the map, not the manual.
The deeper Observatory pages do the detailed work. This page only explains the product idea, the input-analyze-output loop, and where to go next.
# Mental model enterprise exports -> Observatory -> process truth -> decisions, dashboards, audits # Ownership model your infrastructure your outputs your adoption path
START WITH THE RIGHT QUESTION
TECHNICAL ROUTE
Can this fit our stack?
Start with architecture if you need to understand the engine, deployment model, open edges, closed core, and auditability.
BUSINESS ROUTE
Can this expose value loss?
Start with business value if you need to understand the diagnostic layer, economic signals, and management use cases.