LazyFox was built for regulated, sensitive, and complex enterprise environments from day one. Your data never reaches a model. Your permissions never leave your control. Your knowledge layer runs wherever your compliance requirements demand.
Most AI infrastructure asks you to make a trade: capability in exchange for exposure. Data gets sent to external APIs. Context gets processed by third-party models. Definitions get stored in someone else's system.
LazyFox is built on a different premise. Your raw data never touches a model. Your knowledge layer stays under your control. And your permissions follow your existing organizational structure, not a new one you have to build.
customer_id: C-00482
amount: € 84,720.00
status: "offen"
due_date: 2026-07-31
amount: DECIMAL(10,2) · range €0–€2M · monthly
status: ENUM · ["offen","bezahlt","storniert"]
due_date: DATE · 30d avg cycle · no nulls
SELECT SUM({{amount_var}}) FROM invoices
WHERE status = {{status_var}}
AND due_date < {{date_var}}
SELECT SUM(amount) FROM invoices
WHERE status = 'offen'
AND due_date < '2026-08-01'
LazyFox reads all data it has access to in your connected systems, actual records, alongside schemas and field names. This is what makes deep semantic indexing possible: understanding data ranges, value patterns, formats, update frequencies, and relationship context across all your connected systems.
What LazyFox never does is send that data to a model. Before any query reaches an AI model, sensitive field values are replaced with typed abstract variables. The model receives statistical and structural representations of your data (ranges, formats, contextual metadata) and generates code against those abstractions.
Real values are substituted back only at execution time, inside your environment. The model never sees them.
LazyFox ships with a full enterprise permission model, configurable at every level of the system, from which semantic units a team can view down to which columns are visible in AI-generated query results for a given role.
Existing permission structures from your identity provider map directly into LazyFox. You don't rebuild access control from scratch.
Deployment model is a configuration choice, not a separate product. Feature parity is maintained across both options.
Fully managed by LazyFox. Data processed and stored within your selected cloud region. Suitable for organizations with standard enterprise compliance requirements.
LazyFox deployed entirely within your network boundary. No data, metadata, or semantic definitions leave your environment. All AI model calls route through your own model infrastructure or approved enterprise API agreements.
AI token consumption is an operational and financial risk that most enterprises underestimate, until a single feature triggers unexpected reasoning loops and exhausts a month's budget in a day.
LazyFox gives administrators granular control over every AI service in the platform: enable or disable individual services, set budgets and rate limits, monitor consumption by service, user, and department, and configure which model handles which task type.
You always know what your AI infrastructure is spending. And you always control it.
LLMs are powerful at understanding intent, interpreting ambiguous language, and generating complex logic, but they are, by design, probabilistic. They produce outputs that are likely correct, not outputs that are guaranteed correct.
For regulated industries, "likely correct" is not a standard you can operate on.
LazyFox uses AI at the reasoning layer, to interpret a natural language question, understand semantic context, and generate the appropriate query logic, but executes against your actual data using code. That means the same query, against the same data, always produces the same result.
Every change to your semantic layer is logged: who changed it, what it was before, what it is now, and when. For organizations where auditability is a compliance requirement (financial services, healthcare, public sector) auditability is the baseline.
Talk to our team about your specific compliance requirements, deployment constraints, or data residency needs, before you evaluate, not after.