Eliminate the AI-Pilot-to-Production Gap

Make enterprise AI actually work.

LazyFox gives AI a shared understanding of your business.
Works with your existing stack. Live in days. No migration required.

LazyFox. Semantic Management
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from the team that built AI-solutions used by

Mercado Libre
SIEMENS
Mercedes-Benz

Three barriers stand between enterprises and AI that actually works.
LazyFox removes all three.

01 — Data

All your data (securely) accessible.

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AI can only work with the data it has access to. Currently, that’s less than 1% (IDC). Adding one additional data source: a 6-12 months project.

LazyFox makes all your data accessible to AI - without sharing any of it with models.

02 — Meaning

Context for consistent meaning.

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Results from AI are only as good as the context you can give it. Currently, every enterprise system holds its own truth, stitched together by human APIs. Throw AI on it and it picks a different number every time.

LazyFox resolves every metric to a governed answer automatically - no manual cataloging, no business-IT coordination bottleneck.

03 — Trust

Observable, current and yours.

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An answer is only trustworthy if it stays current and can be explained. Compounding institutional knowledge is only valuable if you own it. Currently, that requires trusting several black boxes.

LazyFox reconciles every answer automatically as your data changes, and keeps the resulting knowledge yours - deterministic, audited, and portable across any model.

What this gives you.

01

Answers You Can Defend.

Governs what every metric means across your stack, so every AI tool, agent, and report returns the same governed answer: deterministic, versioned, role-aware, with an audit trail your CFO signs and your regulator accepts.

Learn about trusted answers →
SAP · ERP
€12.4M
Recognized, IFRS 15
Conflicts
Salesforce · CRM
€18.2M
Contracted ARR at close
Conflicts
Looker · BI
€15.1M
MRR × 12, active subs
Forecast
Governed definition
"Revenue" → €18.2M
Salesforce contracted ARR · Context: CFO Q3 report · v4.2 · Approved Finance · 3 other definitions available by context
02

Your Institutional Memory. Owned by You.

Every resolved conflict and approved definition becomes governed precedent: a knowledge graph of what your business means that compounds inside your company, not inside your model vendor.

Learn about staying vendor-agnostic →
Mo 1
Schema mapped. First definitions indexed from existing systems.
Mo 3
Cross-system conflicts resolved. Logical layer active across 3+ sources.
Mo 6
Contextual layer live. Role-based meaning versioned by team and use case.
Mo 12
Institutional memory deepening. New queries cost a fraction of month-one.
Mo 24
Full knowledge graph. Every new system inherits governed definitions on connect.
03

Live in Hours. No migration.

Connects read-only to your existing systems and builds a governed semantic layer from what's already there.

Learn about the data catalog →
Without LazyFox
🔄
ETL pipeline build
8-16 weeks
📦
Data migration
€150K-€500K
🚫
Governance layer
Not included
With LazyFox
🔌
Read-only connection
Minutes to connect
🧠
Semantic layer built
From existing data
Governed & live
Same day
04

Governance That Stays Current.

Monitors every connected source for conflicts and drift before your AI inherits them, versioning every approved definition automatically.

Learn about the data catalog →
Definition Monitor · "Revenue"
Day 0
Definition created
Source: Salesforce contracted ARR · Approved: Finance
v1.0
Day 47
Schema change detected
SAP S/4HANA: orders.amount column type changed
Drift alert
Day 47
Conflict surfaced
3 dependent metrics affected · Flagged for review
Review required
Day 48
Definition updated & versioned
All dependent queries auto-updated · Board report unaffected
v4.2 · Resolved
05

Business-Owned. No IT Gate.

Enables your domain experts in natural language, eliminating the IT-Business coordination bottleneck on every update.

Learn about natural language analytics →
Before, every update
Business expert identifies change
Knows what it should mean
Files IT ticket
Joins the queue
IT writes YAML / SQL
Interprets the requirement
Data team deploys
Reviews, tests, pushes
3-6 weeks per change
After, any update
Domain expert describes change
Natural language
Semantic manager approves
One-click review
Live & versioned
Instantly propagated
Hours. No IT required.
06

Works Beneath Every Tool You Already Have.

Governs the context layer beneath Claude, Gemini, Glean, Slack AI, making each one more accurate without a new interface or rollout.

Learn about integrations →
AI Tools (any)
Claude
Gemini
Glean
Slack AI
+ any
Context governed here
LazyFox Semantic Layer
Governed definitions
Version control
Drift detection
Read-only connection
Your existing data (no migration)
SAP
Salesforce
MongoDB
Databricks
+ more
07

And the Economics Work.

Definitions are built once at setup; every governed query then runs directly from code with zero per-query model calls. Routine token spend drops 60-80%, so the layer funds itself.

Learn how to cut your token bill →
Without LazyFox
~€40K
per month · 10K daily queries
Per-query LLM calls.
Cost scales with volume.
Context re-injected every time.
With LazyFox
~€8K
per month · same query volume
Indexed once at setup.
Queries run from code.
Cost bounded by definitions.
Average token cost reduction on routine enterprise queries
60-80%

Recent Highlights

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About this page

What is LazyFox?

LazyFox is an enterprise AI adoption platform that reconciles what data means across every system a business runs, so AI tools return the same, correct answer every time. It sits read-only on top of the stack a company already has (Databricks, Salesforce, SAP, Snowflake, MongoDB, and more), rather than requiring a migration to a new platform.

Enterprises use LazyFox to remove three barriers to reliable AI: reaching data that's locked away, resolving conflicting metric definitions, and producing answers that can be explained and audited. It goes live in days, with no data shared with the underlying AI models.