Built by the people who lived the problem.

John has spent his career in outsourced IT — working both as the customer managing multi-billion-dollar environments and as the provider delivering services across healthcare, financial services, and aerospace. That dual perspective is what shaped FogLifter's core philosophy: both sides of a relationship need the same trusted data.

He saw organizations disputing thousands of servers every billing cycle, watching chargeback arguments consume executive time, and accepting invoices within 20% of last month because verifying them cost more than the error. His response was the Three Cs framework — Count, Caliber, Cost — and the conviction that you can't manage what you can't count, you can't improve what you can't measure, and you can't allocate what you can't attribute.

Dan built the ontology — the semantic database at the core of FogLifter that encodes eight years of knowledge about how enterprise IT systems actually work. Where traditional tools do extract, transform, load, the ontology inverts the process: bring data in as-is, then apply context. Tower structures, naming taxonomies, asset relationships, and classification rules are encoded so the data is interpreted correctly without manual mapping.

His conviction is that AI's bottleneck isn't model intelligence — it's data quality. Every NLQ answer in FogLifter is grounded in validated, ontology-contextualized records. Not because the AI models aren't extraordinary — they are — but because AI on bad data just produces confident nonsense faster.