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AI Agents for Enterprise
Data Management

Automate data quality, discovery, and governance with Claude's multi-agent architecture. Deploy specialized agents that work in parallel, each with isolated context and domain expertise.

The Challenge

Data Teams Are Drowning

Manual processes can't keep pace with the scale and complexity of modern data estates.

Quality Rules Lag Behind

Data evolves faster than teams can write validation rules. Edge cases slip through, trust erodes.

Manual Profiling Bottlenecks

Analysts spend weeks profiling new sources. Onboarding becomes a barrier to data-driven initiatives.

Governance Is Reactive

Compliance audits reveal gaps too late. Sensitive data classification happens after the breach.

Our Solution

Multi-Agent Architecture

Specialized AI agents working in parallel, each with isolated context and domain expertise.

Orchestrator
Claude Opus 4.5 • Delegates & Coordinates
MCP Servers: Snowflake • Databricks • PostgreSQL • AWS
90%+
Performance gain vs single-agent
60%
Reduction in manual effort
5
Specialized agents
Parallel execution
Process

How It Works

From assessment to continuous automation in three phases.

1

Assess

We evaluate your data estate, identify high-value automation targets, and design the agent architecture.

2

Configure

Deploy agents with your specific tools, platforms, and business rules. Connect via MCP to your data infrastructure.

3

Operate

Agents run continuously with human oversight. Monitor performance, refine rules, and scale across your estate.

Applications

Use Cases

Real-world scenarios where Claude agents accelerate data management.

New Source Onboarding

Automate the analysis, profiling, and documentation of new data sources.

Discovery → Profiler → Recommender → Modeller

Estate-Wide Profiling

Run parallel profiling across your entire data warehouse to establish quality baselines.

Profiler (parallel) → Recommender → Governance

Compliance Automation

Continuously monitor data assets for regulatory compliance and sensitivity classification.

Discovery → Governance → Alerting

Data Product Creation

Accelerate the creation of trusted data products with automated quality and documentation.

Modeller → Profiler → Recommender → Governance
Technology

Integration Stack

Built on Claude Agent SDK with MCP connections to your data infrastructure.

Core:Claude Agent SDK
Core:Model Context Protocol
Platform:Snowflake
Platform:Databricks
Platform:PostgreSQL
Cloud:AWS
Output:Great Expectations
Output:dbt
Output:OpenLineage
Resources

Learn More

Deep-dive into the technical details and get started with agent templates.

Download Whitepaper

38-page technical report on Claude agents for data management.

Download PDF

Technical Deep-Dive

Read the full article on agent architecture, MCP integration, and orchestration.

Read Article

Agent Templates

Access our agent definitions and MCP configurations as part of your engagement.

Request Access

Ready to Automate Your Data Management?

Book a discovery call to assess your automation opportunities and design your agent architecture.