Data inputs
Infrastructure, cloud, apps, networks, billing, CMDB, ticketing, and supporting operational systems.
How It Works
From fragmented source records to governed answers.
The platform ingests enterprise records, normalizes and reconciles them, models relationships through its ontology, and then delivers dashboards, workflows, exports, and NLQ on top of the same trusted foundation.
From raw records to trusted answers
1
Ingest Cloud, CMDB, billing, service, infra
2
Normalize Map records, reconcile keys, refresh truth
3
Correlate Connect assets, owners, costs, risk, SLA
4
Answer Dashboards, workflows, NLQ, exports
How the platform turns data into decisions
FogLifter® follows a clear sequence — from ingestion through normalization, correlation, and delivery — so every answer your teams get is traceable back to a validated source.
Normalization and validation
Records are mapped, cleaned, reconciled, refreshed, and checked for confidence gaps.
Correlation and analytics
FogLifter links costs, assets, services, owners, policy, and performance signals together.
Dashboards and workflows
Users move from evidence to action through role-specific views, review paths, and exports.
AI / NLQ layer
Natural-language questions operate on the same modeled, validated data instead of a disconnected search index.
Validation and reconciliation
Surface mismatches across systems. Track, investigate, and resolve them before they become billing disputes or audit findings.
Why context makes the difference
Most platforms connect data and leave interpretation to the user. FogLifter's ontology understands how IT records relate to each other — so costs tie to services, services tie to assets, and assets tie to owners — giving every answer a defensible chain of evidence behind it.
Why the ontology matters
FogLifter’s ontology captures how enterprise IT data behaves in the real world. It helps the platform understand what a service is, what assets support it, who owns it, what it costs, which provider touches it, and what policy or SLA expectations apply.
- It makes correlation more accurate.
- It improves categorization and counting consistency.
- It gives NLQ a better frame for interpretation and answer generation.
Ontology-driven intelligence
FogLifter’s ontology helps the platform understand how records relate, so NLQ can answer in context instead of returning disconnected keywords.
Explore each layer in depth
Each section of the platform has its own page — for evaluators who want to go deeper on analytics, integrations, security, or AI before the next conversation.
Analytics
See how FogLifter supports cost, asset, service, and risk analytics.
Integrations
Review the types of systems FogLifter connects to and why that matters.
Security
Understand the governance and control story for enterprise use.
AI / NLQ
See how natural-language questions stay grounded in the platform’s model.
How FogLifter works
From fragmented enterprise data to trusted operational and financial insight — connect inputs, validate records, correlate cost and service relationships, and deliver role-specific dashboards and workflows.
IT Environments
One foundation for internal and outsourced IT.
FogLifter helps internal teams gain a unified view of assets, costs, and service performance, while outsourced environments benefit from independent validation of inventory, billing, and SLA outcomes. Either party in an outsource relationship can contract for FogLifter separately — and both see the same trusted data.
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