LazyFox governs what every metric means across your entire data stack, so every AI tool, every report, and every query returns a consistent answer.
No migration. No SQL. No data team bottleneck.
Why AI still isn't delivering
It's costing more than it should.
Every AI query that hits ambiguous data gets reprocessed, retried, or escalated. Redundant tokens, hallucinated answers, manual corrections, the meter is running.
Nobody trusts the answers.
The same metric (revenue, churn, qualified lead) means something different in every system. AI inherits every inconsistency without noticing. Employees go back to spreadsheets. AI adoption stalls.
Business-IT coordination bottleneck.
Every update to a business definition requires IT and business to co-author it. That coordination bottleneck blocks every AI initiative that follows.
The solution
Governs what every metric means across your stack, so your AI tools, reports, and queries all return the same answer. No hallucinations. No conflicting numbers.
Learn about trusted answers →Connects read-only to your existing systems and builds a governed semantic layer from what's already there.
Enables your domain experts in natural language, eliminating the IT-Business coordination bottleneck on every update.
Monitors every connected source for conflicts and drift before your AI inherits them, versioning every approved definition automatically.
Every resolved conflict, every approved definition, every versioned update deepens the knowledge specific to your business, within your company.
Governs the context layer beneath Claude, Gemini, Glean, Slack AI, making each one more accurate without a new interface or rollout.
Learn about integrations →Definitions are built once at setup. Every subsequent query runs directly from code, no per-query model calls, reducing token costs 60–80%.
Learn how to cut your token bill →Genie Ontology is a genuine step forward. Three analysts explain why context isn’t correctness, and what an architecture built for this gap looks like.
Read → Thought LeadershipSatya Nadella’s case for why the enterprise that owns its semantic layer (not the one that picks the best model) wins the AI era.
Read → Researcha16z draws the line between what labs already own and the vertical complexity that compounds into a durable enterprise moat.
Read →Book a demo
We connect read-only to one of your existing systems and show you trusted, context-aware answers built from your own data. No slides. No migration required.
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