These are the questions that come up most often during evaluations — answered directly so you can keep moving without waiting for a call.
No. FogLifter is designed to strengthen the data those platforms consume — not compete with them. If you have Apptio® for TBM, ServiceNow® for ITSM, or Cloudability® for FinOps, FogLifter validates the underlying asset and service counts those tools depend on. It improves the confidence and context of the records flowing into them.
Cost confidence, inventory trust, invoice validation, service accountability, and provider evidence. Most customers start with one of these and expand. The common thread is that bad decisions come from unverified data — FogLifter gives you verified numbers so arguments become evidence-based conversations.
IT finance, operations, service management, provider governance, security, and executive stakeholders. Each role enters through a different view — the CIO Heat Map, Business Insights, Vector View, or Validation View — but everyone shares the same validated data foundation underneath.
Both. FogLifter works for enterprises running internal IT as shared services, enterprises with one or more outsourced towers, and the service providers themselves. Either party in an outsource relationship can contract for FogLifter separately. The platform reconciles records across all parties into a single correlated model.
Virtualization and cloud have made assets ephemeral and elastic. A VM can come in and out of existence in hours. Containers may or may not count as servers. Allocated storage doesn't equal consumed storage. If you measure only as a snapshot in time, you're missing the reality. FogLifter tracks these changes over time — measuring deltas across months — so you know what your actual compute, storage, and service footprint really is.
Caliber is quality of service. Most organizations only measure service when something breaks badly enough for users to complain. FogLifter chains SLAs to KPIs to SLOs and bundles individual infrastructure components into service-level measurements. Instead of knowing that your NAS was up 99.99% while the app was slow, you measure the end result — like clinician application availability or consumer banking uptime — which is what the business actually cares about.
Cost of assets is the per-unit price — the cost per gigabyte of storage or per server. Cost of service is the iceberg. It includes underutilization, the human effort required to maintain optimal performance, the cost of KPI discrepancies, and the financial impact of delays. TBM and FinOps tools measure the unit. FogLifter layers on the nuance of what it actually takes to deliver, monitor, and right-size a service.
FogLifter compares records across all your systems of record and surfaces the discrepancies. Some differences are real errors that need fixing. Others are ephemeral assets — VMs or containers that come and go — which are allowed variances. FogLifter distinguishes between the two, so your teams can focus on actual data quality issues rather than arguing about elasticity.
Because garbage in, garbage out. Ingesting data without context just creates a larger pile of conflicting information. Hitachi® labels a storage line one way, Dell™ EMC labels it another. Without a semantic layer that understands these relationships, you're making pivot tables on top of contradictions. FogLifter's ontology provides the context that raw ingestion tools don't.
Traditional tools do extract, transform, load — they reshape data before storing it. FogLifter does extract, load, then transform. The data comes in as-is from your systems of record, and the ontology — a semantic database trained on IT systems over eight years — applies the context afterward. It encodes tower structures, naming taxonomies, asset relationships, and classification rules so the data is interpreted correctly without manual mapping.
No — and this is a key differentiator. FogLifter normalizes naming conventions, reconciles overlapping keys, and flags conflicts at ingestion. Data quality issues are surfaced and tracked rather than silently inherited. As one customer put it, "We're the Noah's Ark of IT — we have at least two of everything." FogLifter was built for exactly that reality.
By providing a single reconciled record that every party — IT operations, business units, finance, service providers — can see and comment on. Suspicions like "Are you sure you counted that right?" become evidence-based conversations. The Validation View gives both sides visibility into every discrepancy, its cause, and its resolution status. Over time, this replaces distrust with operational confidence.
Most NLQ implementations connect a language model to whatever data you have — fragmented, inconsistently tagged, spread across spreadsheets and CMDBs. The AI answers confidently, but it has no way to distinguish between an authoritative record and a three-year-old spreadsheet someone attached to a ticket. FogLifter contextualizes and validates every data point before a single question gets asked. The AI is grounded in verified data, not guesswork.
Reports are one-shot. NLQ on verified data lets you follow a thread — a question reveals something that prompts another question, which goes deeper, and each answer builds on the last. In traditional reporting, every follow-up requires another ticket, another analyst, another wait. With FogLifter NLQ, that loop collapses. You stay in context and explore iteratively in a single session.
Yes. FogLifter NLQ lets you create dashboards from conversation. When an iterative query session surfaces a view worth preserving, you can save those results as a permanent, reusable dashboard — so the insight you discovered through questions becomes a standing operational tool without rebuilding it from scratch.
The data. As Dan Jost, FogLifter's Chief Architect, puts it: "The bottleneck isn't the AI model. The models are extraordinary. The bottleneck is the data they're operating on." NLQ without validated data underneath is just a conversational interface on top of whatever you happened to connect it to. FogLifter solves the data problem first — then AI does what it does well.
Because both sides are working from incomplete information. The business unit receiving a chargeback bill assumes it's correct as long as it's within 20% of last month — it's more trouble than it's worth to verify. The party issuing the bill is calculating price times quantity from their own system of record. Over time, these small unchecked differences compound. FogLifter automates that verification so neither side has to just trust the number.
The Validation View puts both parties — whether it's IT and a service provider, or IT and a business unit — into a shared workspace where every discrepancy is visible. Both sides can comment, flag items, agree or disagree, and track resolution over time. If you argued about the same server last month and it's the same answer, the system shows that — so you can fix the root cause upstream instead of re-litigating every cycle.
Shared assets — where multiple business units consume the same application, storage, or data — are one of the hardest allocation problems. FogLifter correlates across dimensions that wouldn't be immediately obvious: network endpoints, server access patterns, software license utilization, even geographic access points. Instead of peanut-butter-spreading costs evenly, you allocate based on actual consumption evidence.
One of the largest healthcare organizations in the country had 52,000 production servers and was routinely disputing 3,000 to 5,000 of them — 7% to 10% of the total environment — with their service provider every single month. FogLifter's Validation View allowed both parties to comment, flag, and resolve each discrepancy. Disputes that used to fester now resolve in the same cycle, and the relationship between customer and provider fundamentally improved.
FogLifter follows a workshop-to-MVP model. Data sources are selected, desired logic is mapped, and an MVP is proposed with implementation costs. Initial data ingestion and validation can begin within weeks. Full correlation across multiple source systems typically takes 60–90 days, depending on the number of systems and the complexity of naming and relationships. From there, additional functions are built iteratively.
FogLifter completely integrates with ServiceNow® — both ITAM elements (HAM Pro, SAM Pro, Cloud Insights) and ITOM elements, portfolio management, and other modules. FogLifter places a token in ServiceNow®, which uses a rigid SQL-based data architecture, while FogLifter operates on a noSQL, unstructured approach that allows assets to change attributes over time. This lets you pass audits while tracking real-world elasticity that ServiceNow®'s structure can't accommodate alone.
FogLifter has validated 75+ enterprise data sources through API collectors and secondary ingestion (spreadsheets, CSV exports, text files, email). Deployment tiers scale from 10 data sources (Base) to 50 (Plus) to 100+ (Professional). Customers in production have connected 40+ IT reporting systems in a single environment, spanning 81 countries and 52 currencies.
Yes. FogLifter supports governed API access for custom integrations, automated exports, and downstream consumption by TBM, ITSM, and analytics platforms. Data flows bidirectionally — FogLifter can both consume from and feed back into systems like ServiceNow®, Apptio®, and cloud FinOps tools — so validated data reaches every system that needs it.
FAQs cover the common questions. The pages below go deeper on capabilities, evidence, and value.
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