AI token costs have become the fastest-growing uncontrolled expense at companies across every sector. LazyFox cuts the waste at the source, before a single token leaves your system.
"Five or seven percent of every knowledge worker’s salary and twenty percent of every engineering salary as additional token costs."Rory O’Driscoll, Scale Venture Partners, on AI token costs as a share of payroll, 20VC Podcast, 2026
Developer tooling gets the attention. But the real token cost explosion is in your knowledge worker population (analysts, ops, finance, sales) who ask the same questions hundreds of times a day.
Knowledge workers don’t ask novel questions. They ask the same questions (about revenue, pipeline, invoices, reports) over and over. Each routine query hits the model fresh, consuming the full token cost every single time. A one-off question that should cost nothing costs $1,000 in tokens when the underlying data is ambiguous.
Only 32% of companies can attribute even half their AI bill to a specific team, product, or use case. The rest receive a single invoice with no breakdown. Without attribution, and without understanding which queries are driving waste, there is no lever to pull when costs spike.
When AI agents hit ambiguous data, field names in different languages, inconsistent status codes, conflicting definitions across systems, they don’t fail gracefully. They retry, re-plan, and re-query. Each ambiguity multiplies token consumption invisibly, at runtime, across every knowledge worker query that touches that data.
When AI agents encounter data with unclear meaning, field names in different languages, inconsistent status codes, conflicting schemas across systems, they don’t fail gracefully. They consume more context trying to resolve the ambiguity, generate longer hedged outputs, and often retry the query multiple times.
This semantic inefficiency is baked into every enterprise data stack. It was never a problem when humans read the data. It becomes an exponential cost driver when AI agents do.
"People go nuts with Claude: one person spent a thousand dollars in token costs over one weekend, on a report you can build with your standard CRM."
Head of RevOps & Enablement, mid-market SaaS companyLazyFox sits between your existing systems and your AI layer, resolving meaning in real time, so every token your AI consumes is spent on actual reasoning, not disambiguation.
LazyFox integrates read-only with MongoDB, your ERP, CRM, and any connected data source. No data is moved, copied, or migrated. Deployment takes 30 days.
Read-only · 30-day deployThe platform builds a cross-system semantic graph, reconciling field names, status codes, and entity definitions simultaneously across all connected sources, not one system at a time.
Runtime · No ETLWhen your AI agents query any connected system, LazyFox resolves ambiguity before the query reaches the model. Fewer context tokens. Fewer retries. Predictable, measurable cost per query.
Measurable ROIAt 36% annual growth (the current industry rate) a typical mid-market AI deployment compounds into a nine-figure annual problem within a single planning horizon.
"From nothing two years ago, this is the largest single external spend that every software company is making."
Jason Lemkin, SaaStr: 20VC Podcast, 2026Prompt compression, caching, and FinOps tooling reduce spend at the margin. Semantic governance eliminates the underlying waste before it reaches the model.
| Capability | Prompt Compression | FinOps / Tagging | Data Migration | LazyFox |
|---|---|---|---|---|
| Reduces token consumption | ~ Marginally | ✗ Visibility only | ~ Indirect | ✓ At the source |
| Works across all connected systems simultaneously | ✗ | ~ With tagging effort | ✗ One system at a time | ✓ Cross-system, runtime |
| No data migration required | ✓ | ✓ | ✗ Migration IS the product | ✓ Read-only |
| 30-day deployment | ✓ | ~ Months of tagging | ✗ 12–24 months | ✓ |
| Measurable token cost per query | ✗ | ~ Post-hoc only | ✗ | ✓ Real-time |
| Eliminates semantic ambiguity at runtime | ✗ | ✗ | ~ If done correctly | ✓ Runtime resolution |
See where your knowledge worker queries are burning budget, what’s recoverable, and what a 90-day reduction path looks like.
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