AI-Ready Data Engineering by Default

Data Models & Data Sets:
Define and manage enterprise data structures and curated datasets without heavy coding.

Enterprise Knowledge Bases:
Create structured, unstructured, and hybrid knowledge bases for AI reasoning and retrieval.

Ontology & Relationships Builder:
Model real-world business relationships directly into data and graphs.

Structured and Unstructured Intelligence

Structured & Operational Data Intelligence:
Operate on relational, transactional, and time-series enterprise data with context.

Structured & Unstructured Data Intelligence:
Unify documents, text, images, and metadata into shared enterprise context.

Unified Context & Modeling Layer:
Eliminate silos by operating on one governed, real-time enterprise context.

Data Quality, Transformation, and Pipelines

Data Quality & Validation:
Detect anomalies, inconsistencies, and missing values automatically.

Transformation Pipelines:
Clean, enrich, and reshape data for analytics, ML, and agents.

Graph and Relational Transformations:
Translate tabular data into graph-ready intelligence without re-engineering pipelines.

Automation for Data Engineering and Science

Reduce manual effort and operational risk:

Automation & Transformation Tools:
No-code tools for recurring data preparation and enrichment workflows.

Event-Driven Processing:
Trigger transformations in response to data changes and business events.

Reusable Data Assets:
Models, pipelines, and logic shared across teams and applications.