Connect a database, document store, or PDF corpus and LazyFox discovers every table, collection, and document, infers every relationship, and generates semantic metadata from the data itself. No manual tagging. No documentation sprints. No consultants.
Traditional data catalogs rest on a false premise: that someone has time to manually tag every table, write column descriptions, and keep them current after every release. In practice, that work never gets done. IDC puts less than 1% of enterprise data in active AI use today, most of it unstructured and invisible to the tools meant to query it.
LazyFox generates your catalog the moment you connect a source: a relational database, a document store like MongoDB, or a folder of PDFs. It scans schema and content, infers what each asset means and how it relates to others, and builds a versioned semantic layer that stays current as your data evolves. Your team doesn't write a word of documentation.
Six capabilities that run automatically when you connect a source, and keep running as your data changes.
Every table, view, column, index, partition, and clustering key found and catalogued automatically. Table sizes, row counts, and analytical profiles are included so you know what you're working with before you write a single query.
Schema · Profiling · InstantLazyFox samples each table and runs AI enrichment over every column, generating plain-language descriptions, inferring data types, identifying possible values, and recognising date formats. No human writes a word of it.
AI-generated · Column-levelHard foreign keys are just the start. LazyFox uses naming patterns, data samples, and LLM reasoning to detect soft joins (relationships your database never declared but that exist in practice). Each one comes with a confidence score and can be reviewed or overridden.
Hard + Soft · Confidence-scoredTables are grouped into logical domains: customer data, transaction data, product catalogue, and so on. When someone asks a business question, LazyFox routes it to the right cluster immediately rather than scanning every table from scratch.
Domain-aware · Query routingEvery change to the semantic layer is versioned with a full diff. When a table's meaning shifts (new columns added, descriptions updated, relationships changed), reports and queries built on the previous version are flagged as potentially obsolete.
Versioned · Diff · Report flagsEvery indexing run, edit, and query is logged. Access is enforced at the column level, scanning is strictly read-only, and the same governance controls extend to unstructured sources: PDFs and document stores receive the same audit trail, access policies, and lineage tracking as any relational table.
Read-only · Column-level · LoggedWhen you connect a source, LazyFox runs a single indexing job. It doesn't need a schema diagram, a data dictionary, or a briefing from the people who built it. It figures it out from the data itself.
The result is a semantic layer that covers every asset in your data source: every table, collection, or document, what it contains, what each field or section means, how it relates to other assets, and how confident those relationships are. It's built once, updated automatically, and available to every query from that point on.
Every change to a table's semantics is tracked as a version. When a column changes meaning, a relationship is dropped, or a new field appears, LazyFox generates a diff and surfaces it before that change quietly breaks a report or returns a wrong answer.
Any report or dashboard built against a previous version of the semantic layer is automatically flagged. Your team sees exactly what changed and can decide whether to regenerate or accept the existing output.
This matters most in regulated environments where data lineage must be documented. A stale definition has audit consequences, not just analytic ones.
The Data Catalog is built for people who have been living with undocumented data, and for the AI that needs to query it reliably.
LazyFox connects to relational databases, document stores, and unstructured file sources used in financial services and enterprise environments: on-premise, cloud-managed, or hybrid. That includes PDFs, which LazyFox opens to structured AI access alongside your transactional and operational data.
See all integrations →We'll connect a live data source in the demo (a database, document store, or PDF corpus) and walk you through what the catalog generates: tables, collections, enrichment, soft relationships, and all.
The LazyFox Data Catalog automatically discovers, documents, and semantically enriches enterprise data assets. Connect any data source (a relational database, a document store like MongoDB, or files such as PDFs) and it scans every table, collection, and document, infers relationships, and generates business-readable metadata from the data itself, with no manual tagging, documentation sprints, or consultant-led projects needed. The catalog is live within minutes of connection.
The catalog stays current automatically. When schemas change or new tables are added, it updates. When business users change a metric definition, it reflects that too. There is no re-indexing job to schedule and no documentation sprint to keep things accurate.
LazyFox also covers unstructured data governance: PDFs, document stores, and non-relational sources are catalogued, access-controlled, and lineage-tracked with the same rigor as structured databases. Organizations in regulated industries use this to close the compliance gap that typically exists between their relational data and everything else.