FogLifter® NLQ connects your chosen AI system to your actual company data — safely and securely. The ontology makes queries cheaper and more accurate. No hallucinations. No made-up numbers. No query language or schema knowledge required. If you can ask it, you can answer it.
Four things evaluators want to know about AI in enterprise IT — and how FogLifter answers each one.
Plain-language business questions about cost, assets, services, owners, and performance. No query language or schema knowledge required. No waiting on IT or analysts to build a report. A CFO can ask "why did managed services cost spike last month?" and get an answer grounded in reconciled records — in the same session, not after a two-week reporting cycle.
Answers come from verified systems of record. Calculations are checked against source data. No hallucinations, no guesswork. Every NLQ response traces back to the validated records that produced it — so when someone challenges the number, you can show exactly where it came from. AI confidence without data proof is noise. FogLifter provides the proof.
Start with a question. The answer reveals something that prompts another question, which goes deeper — each answer builds on the last. When an iterative session surfaces a view worth preserving, save it as a permanent, reusable dashboard. Insights shouldn't disappear when the chat ends. In traditional reporting, every follow-up requires another ticket, another analyst, another wait. With FogLifter NLQ, that loop collapses.
NLQ uses your existing security controls and honors your permissions and access rules. A finance user's NLQ session can't surface infrastructure detail they don't have access to in a dashboard. FogLifter works with your approved AI platforms — you choose the model, FogLifter governs the data. No LLM is trained on your data. No customer data leaves your governed environment. Enterprise AI only works inside enterprise guardrails.
AI is only as good as its data — and FogLifter authenticates every source. The ontology provides semantic context so the AI understands what a record means, not just what it contains. The result: hallucinations caused by bad inputs are eliminated before they can reach the answer.
"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."
These are the kinds of cross-domain questions that generic AI can't answer reliably — because the answer requires validated relationships between cost, assets, services, and owners.
The answer needs cost, service, and ownership relationships together — not three separate exports stitched in a spreadsheet. This is the kind of question that surfaced the 25–30% cloud utilization discovery: customers running a fraction of provisioned capacity, invisible until NLQ connected utilization data to service ownership and contract terms in a single query.
The answer depends on billing, asset, and contract evidence — cross-referenced in real time. This is how FogLifter achieved an 85% reduction in billing disputes: NLQ lets both parties query the same reconciled data, see the same discrepancies, and trace them to the source record. The argument ends because the evidence is shared.
Cost of assets is the surface — the per-unit price. Cost of service is the iceberg: underutilization, labor to maintain, KPI discrepancies, and the impact of delays. NLQ on FogLifter's validated model lets you follow the thread from a service-level cost question into the asset, owner, and provider detail underneath — iteratively, in one session, without filing a report request.
The interface isn't the problem. The experience feels intuitive. But underneath, the data is a mess — and the AI has no way to know.
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. Every follow-on question in a traditional reporting environment requires another ticket, another analyst, another wait. The conversational interface is compelling — but without validated data, it's a confident voice reading from unreliable sources.
FogLifter contextualizes and validates every data point before a single question gets asked. The ontology — built over eight years on enterprise IT systems — provides semantic understanding of how records relate: tower structures, naming taxonomies, asset relationships, and classification rules. The AI is grounded in verified data, not guesswork. The iterative loop lets you follow a thread — each answer builds on the last — and save the result as a permanent dashboard. This is the architecture that delivers on the promise of AI in enterprise IT: not smarter AI querying bad data, but verified data enabling AI to do what it does well.
NLQ is the interface. The pages below cover the foundation it depends on — analytics, integrations, and security.
Cost, asset, ownership, and service analytics built on reconciled data.
How FogLifter connects to CMDB, billing, cloud, infrastructure, and ticketing systems.
Enterprise access control, AI data isolation, auditability, and governed data handling.
