01 / UNDERSTAND
Know what exists
Catalog assets, profile real data, capture quality, discover relationships and expose lineage.
Metadata & Intelligence Layer for Enterprise AI
Know your data better.
MagnifyData creates a governed understanding of enterprise data wherever it lives — then gives AI the context it needs to ask the right questions and query the right data.
MAGNIFYDATA / GOVERNED CONTEXT
MAGNIFYDATA
Metadata & Intelligence Layer
UNDERSTAND YOUR DATA
◈Catalog
◫Profiling
✓Quality
⌘Relationships
GIVE DATA MEANING
◉Semantics
▤Glossary
◌Metrics
⌁Knowledge
GOVERN YOUR DATA
◇Governance
▣Policies
◍Security
⤢Lineage
AI CONTEXT
Trusted, governed and enriched context for AI
User Question
“How many active customers did we have last month?”
→
LLM
✦
Generate SQL using context
SQL VALIDATION
Schema ✓ Relationships ✓ Policies ✓ Permissions ✓
SQL Agent Connector
Securely execute against configured customer data platforms
PostgreSQL
SQL Server
Oracle
Snowflake
BigQuery
Databricks
Query Result
Actual data is retrieved from the customer's data platform
Business Answer
Grounded in governed context + real query results
The MagnifyData principle
MagnifyData manages the understanding of data — not the data itself.
Your databases, warehouses and lakehouses remain where they are. MagnifyData builds the metadata, meaning, relationships, quality, governance and context that make those data assets understandable and usable by people and AI.
Your data stays where it already is. Your intelligence becomes connected.
One metadata and governance layer across an existing enterprise data landscape.
02 / GIVE MEANING
Know what it means
Connect technical structures to business terms, semantics, metrics, knowledge and business rules.
03 / GOVERN
Know how it should be used
Bring ownership, policies, classification, permissions, provenance and AI usage into the same context.
Build context before AI
Discover. Understand. Govern. Enable.
AI becomes more useful when business context is retrieved before SQL or answers are generated. MagnifyData assembles that context from the enterprise data landscape.
01
Discover
Find systems, assets, schemas, columns and dependencies.
02
Catalog
Create a governed technical inventory with useful metadata.
03
Profile
Observe what the actual data looks like.
04
Connect
Capture relationships, lineage and dependencies.
05
Give Meaning
Map terms, semantics, metrics and business rules.
06
Build Context
Make trusted context available to AI and applications.
The AI loop
Give AI the context. Let your data platform answer the question.
MagnifyData does not need to become the analytical store. It provides the intelligence layer, generates governed SQL through an LLM, validates the query, and connects back to the customer's source or target platform.
Why the metadata layer matters
Before SQL is generated, the model can be grounded in the enterprise's existing knowledge of its data.
✓
Schema metadataTables, columns, types and technical structure.
✓
RelationshipsKnown links and dependencies across assets.
✓
Business meaningGlossary terms, semantics and metric definitions.
✓
Governance & securityPolicies, permissions, classification and approved use.
✓
Lineage & contextProvenance and surrounding knowledge for better reasoning.
User Question — Natural Language
↓
MagnifyData — Retrieve Governed AI Context
↓
LLM — Generate Target-Specific SQL
↓
SQL Validation — Schema • Policy • Permission Checks
↓
SQL Agent Connector — Secure Execution
↓
Customer Database / Warehouse / LakehouseActual enterprise data remains in the customer's platform
↓
Query Result → MagnifyData / LLM
↓
Trusted Business Answer
Works across your landscape
One context layer across many data platforms.
MagnifyData is designed to understand enterprise assets without forcing a single data-store strategy.
PG
PostgreSQLRelational database
MS
SQL ServerRelational database
OR
OracleEnterprise database
SF
SnowflakeCloud data platform
BQ
BigQueryCloud warehouse
DB
DatabricksLakehouse platform
S3
Amazon S3Object storage / data lake
+
More platformsExtend through connectors
Trust by design
AI should retrieve governed context — not invent enterprise meaning.
MagnifyData separates observed facts, inferred intelligence, business meaning and human-approved governance. That context can then participate in the AI query path.
Retrieve before generateUse existing enterprise context before asking AI to generate.
Human approvalAI enrichment can be reviewed before becoming trusted metadata.
ProvenanceKeep track of where meaning and relationships originate.
Runtime securityKeep access boundaries in the intelligence path.
MagnifyData
Know your data better.
Give AI the context it needs.
Build a metadata and intelligence layer across your existing data landscape — without replacing the platforms that already hold your data.