IDC puts less than 1% of enterprise data in active AI use. The models are not the problem: the data sits locked behind migrations, ETL pipelines, and formats no governance tool can see. Adding a single new source the traditional way takes 6-12 months.
LazyFox removes the barrier. It connects read-only to the systems you already run, builds a semantic layer from what's there, and shares none of your data with any model. Live in days.
Why AI is data-starved
Three structural reasons enterprise AI runs on a fraction of what the business knows.
Warehouses, CRMs, ERPs, document stores: each has its own access model. The traditional answer is to centralize everything first, an ETL pipeline build of 8-16 weeks and a €150K-€500K migration, before AI answers a single question.
The migration taxdbt, Metric Views, and LookML only govern data that has already been migrated to a warehouse. The invoices, contracts, event logs, and MongoDB collections where operational knowledge lives fall entirely outside those layers.
The governance gapEven once built, ETL pipelines are brittle: schema changes break them, maintenance costs compound (€360K/year in tooling is typical), and every fix goes back through the IT queue. Access decays as fast as it's built.
The maintenance trapHow LazyFox removes the barrier
Point LazyFox at your data systems via read-only API connectors: no agent to deploy, no schema migration, no need to centralize data first. No data leaves its home.
One indexing job then builds your catalog. It discovers every table, collection, and document, enriches every column with a plain-language description, and infers relationships, including the soft joins your database never declared. LazyFox detects and versions schema drift, so access doesn't quietly rot after the next release.
Explore the Data Catalog →80% of enterprise knowledge is unstructured, and most of it never reaches AI. LazyFox indexes document stores like MongoDB, PDFs, contracts, and reports through the same semantic layer that governs your structured data, without moving or duplicating a single byte.
Flexible schemas stop being an obstacle. LazyFox enriches and reconciles fields like "rev", "revenue", and "total_rev", then makes them queryable in natural language, with the same audit trail and access policies as any relational table.
Make MongoDB data AI-ready →LazyFox connects across seven integration categories (warehouses, lakehouses, document stores, dbt models, BI tools, CRM and operational systems) and governs meaning across all of them at once. A single metric can draw from multiple systems and return one consistent answer, regardless of where the data lives.
Your stack keeps evolving; you don't rebuild the semantic layer when it does. No lock-in, no rearchitecting.
Accessible ≠ exposed
Making data reachable for AI must not mean handing it to a model vendor. LazyFox reads your data to build governed context. The data itself stays where it lives, under your rules.
LazyFox scans strictly read-only. It writes, moves, and duplicates nothing, and nothing leaves its source system.
LazyFox reads your data to build the semantic layer. It never shares it with a model or uses it for training.
LazyFox enforces access down to column level, and the same policies extend to PDFs and document stores.
Fully managed and ready in days, or fully contained in your own environment. You decide where knowledge lives.
The data barrier is the gap between the data enterprises hold and the data AI systems can actually reach. IDC estimates less than 1% of enterprise data is in active use by AI today, because most of it is unstructured, locked behind legacy systems, or requires migration and ETL pipelines before any AI tool can query it. Adding a single new source through traditional integration takes 6-12 months.
LazyFox removes the barrier by connecting read-only to the systems already in use, structured and unstructured, and building a semantic layer over them without moving or duplicating any data. No data is shared with the underlying AI models, and a new source can go live in days instead of months.
See it live
We'll connect a live source in the demo (a database, document store, or PDF corpus) and walk you through what LazyFox generates: tables, collections, enrichment, relationships, and all.