80% of enterprise knowledge lives in unstructured data. Most of it is in MongoDB. LazyFox connects to your MongoDB collections read-only, builds a semantic layer on top of your documents, and lets every AI tool in your stack query it in natural language, with governed, consistent answers.
"What was net revenue from cancelled orders last quarter, by region?", asked through Claude, Gemini, or any connected tool. LazyFox resolves which revenue, which cancellation definition, which ARR version, automatically, before the query runs.
Document databases are where operational knowledge lives, customer records, product catalogues, event logs, contracts. Flexible schema means rich data. It also means inconsistent field names, undocumented abbreviations, and no governing layer to tell AI what any of it means.
MongoDB's greatest strength (no enforced schema) is an AI's greatest obstacle. "rev", "revenue", "total_rev", "arr_val" can all mean different things across collections, teams, and time. The AI picks one, arbitrarily.
dbt, Databricks Metric Views, and LookML only govern data that has been migrated to a warehouse. Your MongoDB operational data, which is where events, transactions, and customer context actually live, falls entirely outside those layers.
The traditional answer is: migrate MongoDB data to Redshift or Databricks, then govern it there. ETL/Data Warehouse setup costs €360k/year in tooling, takes 6 months to stand up, and breaks every time the schema evolves, which is what MongoDB schemas do.
Custom RAG pipelines on MongoDB retrieve matching documents, but without a semantic layer, they return matches without meaning. When "cancellation" means pre-shipment in one collection and post-delivery in another, retrieval without reconciliation produces contradictory answers.
LazyFox sits above your MongoDB collections and creates a semantic intelligence layer across your documents, without moving or duplicating a single byte. Connect once. Query everything.
LazyFox connects to your MongoDB Atlas instance or self-hosted cluster with read-only credentials. Nothing moves. Nothing migrates. No ETL pipeline to build or maintain.
Setup: ~1 hourLazyFox indexes your collections, identifies active vs. obsolete fields, maps relationships across documents, and enriches every field with a semantic description. Tokens consumed once, here, not per query.
Enrichment: hoursA small programme team reviews and approves metric definitions in plain language. No YAML. No PRs. Conflicts across collections surface automatically before any definition is saved.
First session: ~2 hoursEvery AI tool you already use (Claude, Gemini, Slack AI, your own agents) queries through LazyFox and receives governed, consistent answers. Your team never interacts with LazyFox directly.
Live: same dayEvery traditional approach to making MongoDB AI-ready costs more, takes longer, and still doesn't solve the semantic reconciliation problem. Here's the comparison.
ETL to a data warehouse
€360k/yr tooling · 6 months · breaks on schema changeMove data from MongoDB to Redshift or Databricks, then govern it there. Requires a full ETL pipeline, ongoing maintenance, and breaks every time the MongoDB schema evolves.
LazyFox: connect once, read-only. No pipeline to build. No data to move. Schema changes are detected and re-indexed automatically. Time to value measured in hours, not months. Cost: a fraction of ETL tooling.
Custom RAG pipeline
€360k/yr engineering · breaks on schema change · no semantic governanceBuild a bespoke retrieval pipeline that embeds MongoDB documents and retrieves them by vector similarity. Requires ongoing AI engineering, has no governing layer, and returns matches without meaning.
LazyFox: governed meaning, not just retrieval. The semantic layer resolves what each field means before the query runs. No ongoing engineering. Conflict detection prevents contradictory answers across collections.
MongoDB Atlas Search / Vector Search
retrieves matching documents · cannot resolve what they meanAtlas Search finds documents that match a query. It has no concept of what those documents mean in your business context, and no framework for resolving conflicting definitions across collections.
LazyFox: meaning governance, not retrieval. Atlas Search is a great retrieval tool. LazyFox tells every AI system what the retrieved documents mean, which definitions apply, and which interpretations are governed. Complementary, not competing.
System-by-system data source mapping
€150k–€5M+ · 6–18 months per system · no cross-system reconciliationMap each data source individually to a centralised semantic layer. Each connection is a bespoke integration project. Meaning still lives in system-specific silos with no runtime reconciliation.
LazyFox: connect once, map to all. MongoDB is the entry point. As your organisation surfaces additional semantic conflicts, LazyFox expands to every connected system, without starting a new integration project each time.
These are the queries that were impossible (or dangerously unreliable) before a semantic layer sat above your MongoDB collections.
Revenue fields exist in orders, contracts, and invoices collections, with different field names, different calculation logic, and different team-level definitions. LazyFox reconciles them before the query runs.
Churn means lost logos to Sales, ARR reduction to Finance, and product inactivity to the analytics team. LazyFox governs all three simultaneously and applies the right definition to the right query.
Event logs in MongoDB are where real operational intelligence lives, but they're incomprehensible to non-engineers without a semantic layer to translate field names and status codes into business language.
AI agents querying MongoDB without a semantic layer hallucinate field meanings and produce unreliable aggregations. LazyFox pre-builds governed query templates so agent outputs are deterministic: 100% of the time.
When two MongoDB collections define "active customer" differently (one by last login, one by contract status) LazyFox surfaces the conflict automatically before any AI agent inherits it as a false truth.
MongoDB schemas change. ETL pipelines break. Custom RAG embeddings go stale. LazyFox detects schema changes, flags affected definitions, and re-enriches automatically, keeping your semantic layer current as your documents evolve.
"The limiting factor for AI companies is data entropy:
unstructured data is where 80% of corporate knowledge lives."
Jennifer Li, General Partner AI & Infrastructure · Andreessen Horowitz · December 2025
Connect your MongoDB instance. Have natural language queries with governed answers running the same day. No ETL. No migration. No new interface for your team.