01
Data Catalog
Discover and manage tables, columns, sources, targets, ownership, descriptions, and technical metadata.
Governed data foundation for enterprise AI
Know your data better.
MagnifyData brings catalog, quality, relationships, semantics, governance, and security together into a trusted context layer for enterprise AI.
Discover. Catalog. Profile. Govern. Understand. Give AI the context it needs.
Governed asset context
customer
AI ready
Customer master data • Curated asset
Context graph
customer
orders
revenue
glossary
lineage
The MagnifyData approach
Build the context before you build the AI.
Enterprise AI becomes more reliable when it can retrieve governed, traceable business context instead of guessing what data means.
01
Discover
Find systems, assets, columns, keys, and dependencies.
02
Catalog
Create a usable technical inventory with ownership and metadata.
03
Profile
Measure what the data actually looks like.
04
Relationships
Capture trustworthy links between assets and concepts.
05
Semantics
Add business definitions, metrics, terms, and rules.
06
Knowledge
Make governed context available to search, RAG, and agents.
Platform
Everything needed to understand enterprise data.
MagnifyData connects the technical, business, quality, and governance sides of data into one foundation.
02
Data Profiling
Understand nulls, distinct values, distributions, ranges, and other observed characteristics.
03
Data Quality
Turn observations into governed quality rules, executions, evidence, issues, and remediation records.
04
Relationships & Lineage
Make asset relationships and data movement visible, reviewable, and useful for downstream intelligence.
05
Semantic Layer
Give columns and metrics business names, definitions, synonyms, roles, units, and aggregation meaning.
06
Business Glossary
Connect enterprise terminology to actual data assets so business language maps to governed information.
07
Governance
Manage ownership, review, approval, AI usage, provenance, and auditability as first-class capabilities.
08
Security
Apply tenant-aware policies and column-level controls so downstream users and AI respect access boundaries.
09
AI Readiness
Expose governed context to future RAG, NL2SQL, search, agents, and business actions without making AI the system of record.
Trust by design
AI should consume governed context — not invent it.
MagnifyData separates observed facts, inferred relationships, business meaning, and human-approved governance. The result is context that can be explained and traced.
Retrieve before generate
Ground answers in existing enterprise context before asking AI to generate.
Human approval
AI can suggest metadata, but governance decides what becomes trusted.
Provenance
Keep track of where meaning and relationships came from.
Runtime security
Authorization applies before data context is exposed to downstream intelligence.
Architecture
A foundation AI can actually trust.
MagnifyData creates a governed context layer between enterprise data and AI applications.
From raw data to governed intelligence
Technical metadata, observed quality, business semantics, relationships, lineage, governance, and security converge before AI consumes the context.
- Control-plane metadata stays authoritative.
- AI enrichment is proposal-based.
- Governance remains reviewable and auditable.
- Future agents reuse the same governed query and context foundations.
Enterprise Data Sources
↓
Catalog • Profiling • Quality • Relationships • Lineage
↓
Glossary • Semantics • Metrics • Business Rules
↓
Governance • Security • Approval • Provenance
↓
Governed Context
↓
RAG • NL2SQL • AI Agents • Business Actions
Start the conversation
Know your data. Build AI you can trust.
See how MagnifyData can turn fragmented enterprise data into a governed foundation for analytics and AI.