Your AI stack can only be as reliable as the meaning behind your data. LazyFox is the semantic governance layer that sits above your existing systems, defining, maintaining, and reconciling what your data means across every tool, team, and model you use.
Your data warehouse is clean. Your models are powerful. And somewhere, finance is calculating revenue one way while sales calculates it another, and your AI doesn't know who's right.
When the same KPI means different things to different teams (or different systems) every AI output built on top of it is unreliable by default. Not because the model is wrong. Because the meaning underneath it was never governed.
dbt structures your transformations. Data catalogs document your schemas. BI tools visualize your metrics. None of them govern what your KPIs actually mean across teams, systems, and business events, and none of them detect when that meaning drifts.
Humans reading ambiguous data can apply judgment. AI agents can't. When your AI stack encounters conflicting definitions, it either picks one arbitrarily, hallucinates a reconciliation, or retries at full token cost. Scale makes this worse, not better.
LazyFox operates across three semantic layers (each solving a distinct part of the problem) sitting above your existing systems without touching their structure.
LazyFox indexes the raw shape of your connected data systems, tables, schemas, fields, databases. A high-fidelity map of your connected systems, built without moving a byte. This is where most tools stop. We're just getting started.
Your KPIs, metrics, and business rules. Revenue recognition, attribution windows, churn definitions. Every definition is versioned, validated for conflicts before it's saved, and connected to the systems it applies to. When business events change the rules, LazyFox tracks it.
A controller wants ratios. Marketing wants attribution. Finance wants actuals. The context layer governs multiple valid interpretations of the same metric, without forking your underlying models. Every AI query knows which interpretation applies.
Most AI infrastructure burns tokens on every query, sending context to a model, waiting, paying, repeating. LazyFox is different by design.
Semantic understanding happens at indexing time. Your definitions, rules, and context are captured and stored as governed, queryable knowledge, not re-derived on every request. Once indexed, every downstream query runs directly from code, not from a model.
"You need to have some level of control over your AI infrastructure. The same way you'd have application monitoring, but for the semantic layer."
Head of RevOps & Enablement, mid-market SaaSLazyFox connects to your existing data stack without requiring you to move, consolidate, or restructure anything. Mixed-stack and mid-migration environments work from day one. LazyFox reconciles meaning across all connected systems simultaneously at runtime.
Enterprise knowledge isn't static. Acquisitions happen. Business models change. Teams redefine terms. A semantic layer captured once is already becoming stale. LazyFox runs a continuous governance loop to keep meaning current.
Index your data systems and define your semantic units. KPIs, metrics, business rules, domain terms. Pull in existing documentation from dbt, catalogs, and code repositories.
Index onceEvery new or edited definition is checked for conflicts and ambiguities before it's saved. Overlapping definitions are flagged with plain-language explanations. Nothing lands without passing a validation gate.
Pre-save checksTrack how definitions are queried, challenged, and corrected across your organization. Usage patterns, user pushback, and ticket signals all feed the health map, surfacing blind spots before they cause problems.
Continuous signalWhen meaning drifts, because of a business event, a new data source, or a conflicting update. LazyFox surfaces it. Your governance team sees what changed and approves the resolution. AI queries stay accurate.
Drift resolution"We don't want to have vendor lock-in. If all your knowledge lives with one provider and they change it tomorrow, enjoy your future."Enterprise customer on the LazyFox governance model
We'll walk through how LazyFox connects to your existing stack, where your semantic gaps are, and what a 30-day path to governed meaning looks like.