Multi-Source Semantic Understanding

The Challenge

Enterprise data is scattered across dozens of systems (ERPs, databases, file systems, APIs), each with its own schema, terminology, and logic. Making sense of how all these pieces connect has traditionally required:

Expensive data engineering teams
Months of integration work
Continuous maintenance as systems change
Deep domain expertise

Superatom automates this entirely.

How It Works

Our semantic modeling system performs a multi-stage analysis that transforms disconnected data sources into a unified, queryable knowledge structure.

The Five Analysis Stages

Stage 1: Schema Analysis

What We Analyze
What We Discover
Output

Database names and their purposes
Table names and relationships
Column names and data types
Primary keys and foreign keys
Indexes and constraints
File structures and hierarchies

{
  "tables": {
    "orders": {
      "columns": ["id", "customer_id", "product_id", "quantity", "date"],
      "primary_key": "id",
      "foreign_keys": {
        "customer_id": "customers.id",
        "product_id": "products.id"
      },
      "relationships": ["customers", "products", "order_items"]
    }
  }
}

Stage 2: Sample Data Extraction

We retrieve representative samples from each field to understand actual data characteristics:

Analysis Type	What We Capture	Why It Matters
Value Samples	Random selection of actual values	Understand real data patterns
Edge Cases	Minimum/maximum values	Identify boundaries and outliers
Null Analysis	Percentage of empty fields	Data quality assessment
Format Detection	Date formats, number formats, encodings	Proper parsing and display

Stage 3: Statistical Profiling

Deep statistical analysis of each field: Example Output:

field: order_status
type: categorical
distinct_values: 5
distribution:
  - value: "completed", percentage: 68%
  - value: "pending", percentage: 15%
  - value: "processing", percentage: 10%
  - value: "cancelled", percentage: 5%
  - value: "refunded", percentage: 2%
likely_meaning: "Order lifecycle status"
suggested_visualizations: ["pie_chart", "bar_chart"]

Stage 4: Domain Classification

The system recognizes industry and domain context:

Supply Chain

Inventory levels
Warehouse locations
Transfer orders
Lead times

Retail

Products & SKUs
Customer segments
Sales channels
Promotions

Finance

Transactions
Accounts
Reconciliation
Reporting periods

Domain classification enables:

Automatic terminology mapping (e.g., “SKU” = “product identifier”)
Industry-specific analysis rules
Contextual query interpretation
Relevant visualization defaults

Stage 5: Query Generation

Based on all previous analysis, we generate:

Common Questions

Questions any user would likely ask about this data:

“What are total sales by region?”
“Which products are low on inventory?”
“How has revenue trended this quarter?”

Diagnostic Questions

Questions that help identify problems:

“Which items have negative margins?”
“Where are delivery delays occurring?”
“What’s causing order cancellations?”

Exploratory Questions

Questions that reveal insights:

“What patterns exist in customer behavior?”
“How do different regions compare?”
“What correlations exist between metrics?”

Automated Semantic Modeling

This is a key innovation. Semantic modeling used to require high-level experts and domain specialists. Superatom automates this entirely.

Traditional vs. Superatom Approach

Aspect	Traditional	Superatom
Time to Model	3-6 months	~2 days
Required Expertise	Data architects + domain experts	None
Cost	100K+ consulting	Included
Maintenance	Manual updates	Automatic drift detection
Scalability	Linear effort increase	Near-zero marginal cost

The Zero-Setup Vision

As we add more domain knowledge and verticals, setup cost for new organizations approaches zero: Each new deployment adds to our knowledge base. Each addition makes the next deployment faster.

Handling Data Evolution

Enterprise data isn’t static. Superatom handles evolution:

Drift Detection

Periodic re-analysis identifies schema changes, new tables, modified fields.

Impact Assessment

System determines which queries and dashboards are affected.

Model Update

Semantic model is updated without losing existing customizations.

Notification

Relevant users are notified of changes that might affect their work.

Currently: Manual re-analysis on request Roadmap: Automatic continuous drift detection

Technical Implementation

Connection Layer

Supported Sources

Category	Sources
Databases	PostgreSQL, MySQL, SQL Server, Oracle
Data Warehouses	BigQuery, Snowflake, Databricks, Redshift, ClickHouse
ERP Systems	SAP, Oracle ERP Cloud, Microsoft Dynamics 365
Files	CSV, Excel, JSON, Parquet
APIs	REST endpoints, GraphQL

Getting Started

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Use Cases

Case Studies

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Guides

Multi-Source Semantic Understanding

The Challenge

How It Works

The Five Analysis Stages

Stage 1: Schema Analysis

Stage 2: Sample Data Extraction

Stage 3: Statistical Profiling

Stage 4: Domain Classification

Supply Chain

Retail

Finance

Stage 5: Query Generation

Automated Semantic Modeling

Traditional vs. Superatom Approach

The Zero-Setup Vision

Handling Data Evolution

Technical Implementation

Connection Layer

Supported Sources

Next Steps

Generative UI

Tribal Knowledge

​The Challenge

​How It Works

​The Five Analysis Stages

​Stage 1: Schema Analysis

​Stage 2: Sample Data Extraction

​Stage 3: Statistical Profiling

​Stage 4: Domain Classification

Supply Chain

Retail

Finance

​Stage 5: Query Generation

​Automated Semantic Modeling

​Traditional vs. Superatom Approach

​The Zero-Setup Vision

​Handling Data Evolution

​Technical Implementation

​Connection Layer

​Supported Sources

​Next Steps

Generative UI

Tribal Knowledge

The Challenge

How It Works

The Five Analysis Stages

Stage 1: Schema Analysis

Stage 2: Sample Data Extraction

Stage 3: Statistical Profiling

Stage 4: Domain Classification

Stage 5: Query Generation

Automated Semantic Modeling

Traditional vs. Superatom Approach

The Zero-Setup Vision

Handling Data Evolution

Technical Implementation

Connection Layer

Supported Sources

Next Steps