The Intelligent Data Lifecycle
A continuous loop of planning, engineering, and operations—managed by autonomous agents that never sleep.
End-to-End Automation
Connecting every stage of your data journey.
Phase 1: Plan & Design
Foundational intelligence. Agents understand business context before a single byte is moved.
Requirements Gathering
Agents interview stakeholders to define KPIs.
Assessment & Discovery
Automated profiling of source systems.
Architecture & Design
Blueprints for Medallion architecture generated instantly.
Phase 2: Build & Engineer
Heavy lifting automated. Code generation for ingestion, modeling, and tests.
Ingestion & Integration
Self-healing pipelines for batch and streaming.
Modeling & Development
Physical schemas and semantic models built by AI.
Testing & Validation
Data quality rules generated and enforced.
Phase 3: Operate & Value
Delivering value to users while maintaining system health autonomously.
Deployment & DevOps
CI/CD for data assets.
Managed Operations
SRE agents monitoring performance 24/7.
Agent Capabilities Matrix
Which agent does what?
Requirements
- • Stakeholder interview
- • KPI definition
- • Success criteria mapping
Architecture
- • Platform selection
- • Storage design
- • Compute sizing
Modeling
- • Semantic definitions
- • Relationship mapping
- • DAX / SQL generation
Ingestion
- • Batch & Streaming
- • Schema drift handling
- • Data connector config
Start Your Lifecycle Transformation
Pick an agent and see the difference in minutes.
Browse Agents Hub