Executive-readable stories in a compact format: the challenge, the approach, and the measurable result — across complex, multi-system enterprise environments.
Each story follows a compact challenge-approach-result format designed for executives and evaluators who need the picture in under a minute.
Challenge: fragmented provider and inventory data across multiple billing systems. Approach: validate invoice line items against a reconciled asset and contract foundation. Result: faster monthly review and stronger invoice confidence — with disputes resolved in days instead of weeks.
Challenge: inconsistent asset truth across CMDB, discovery, and billing for 70,000+ servers. Approach: normalize, reconcile, and correlate records to service and ownership. Result: cleaner planning, defensible modernization decisions, and 99.1% record confidence.
Challenge: cloud cost signals lacked ownership and service context across a global portfolio. Approach: enrich spend records through relationship-aware modeling tied to business units. Result: more actionable optimization conversations grounded in validated data.
Each case study covers the environment, the challenge, what FogLifter® changed, and the measurable result.
IT invoice accuracy and cost of service across a complex multi-tower outsourced enterprise — with multiple providers, overlapping billing, and contested contract terms.
Unified IT cost of service across a large integrated healthcare system — 70,000+ servers, 27 petabytes of storage, and three managed service providers.
Telecommunications cost transparency across a global multi-property enterprise — reconciling telecom spend, contracts, and provider performance.
Case studies tell one part of the story. The pages below offer outcome summaries, proof artifacts, and a calculator to model value against your own environment.
Outcome stories organized by the problem that prompted the engagement — cost confidence, dispute reduction, and faster decisions.
Sample metrics, product evidence, and trust markers for evaluators building an internal case.
Estimate the impact of improved data confidence using your own environment as the baseline.
