ChangoAgentic AI Data Platform
An agentic data platform built on an Iceberg-native lakehouse foundation.
Chango Key Features
A self-hosted AI data platform where agents and data live in the same system.
Iceberg-native Lakehouse
Chango isn't just Iceberg-compatible — it's built around Apache Iceberg as a first-class storage model from the ground up. Spark, Trino, and Flink share a single Iceberg catalog (Apache Polaris), with ACID transactions, schema evolution, and time travel built in. Data is stored in standard Iceberg format on open storage — no engine or vendor lock-in.
One Self-Hosted Platform
Object storage, streaming, batch + SQL, OLTP + vector + graph, workflow, and agents — installed, run, and observed from a single control plane.
AI as a First-Class Citizen
A multi-agent runtime and a Model Context Protocol server ship as built-in components. The Ontul MCP server exposes tools like ontul_search_metrics and ontul_describe_semantic_view for metric discovery, natural-language search (Korean 매출 ↔ revenue), and certification metadata — LLMs reach your data through a standard interface, not custom glue.
Ontul Semantic Layer — One Truth for Agents
Ontul, Chango's data engine, ships a production-grade semantic layer. Define a metric once and LLM agents, BI dashboards, and analysts all see the same number from the same definition. Aggregation, JOINs, and RBAC run server-side, so agents only need column names — answering with certified business definitions, not hallucinated formulas.
Native Retrieval Stack
Vector similarity, full-text search, and graph traversal live in the same engine as your transactional data. No external vector DB, no sync gymnastics.
One Identity Plane
A single IAM policy governs SQL queries, agent tool calls, MCP requests, object reads, and stream consumption — consistent across every layer.
Open Engines Welcome
Every engine (Spark, Trino, Flink, Kafka) shares Apache Iceberg as the table format and Apache Polaris as the catalog — under the same IAM, the same observability, and the same operator experience.
Operations You Can Trust
Cluster topology, live metrics, KMS-encrypted state, version-pinned component lifecycle, and audit-ready dashboards in the box.
Use Cases
AI-Native Analytics
Business users ask in natural language; agents reach the data engine through the Ontul semantic layer and MCP, returning answers from certified metric definitions under the operator's IAM.
Retrieval-Augmented Applications
Vector, full-text, and graph relationships in one engine power production RAG and hybrid retrieval — no external vector DB to operate.
Sovereign Data Platform
Run the entire AI data stack on-prem or in your own cloud account — nothing leaves your perimeter.
Multi-Tenant Engine Mesh
Spark, Trino, Flink, and Kafka share infrastructure under one identity and resource-group control, with tenant data kept isolated.
The data platform for the agentic era
Agentic. Sovereign. One Control Plane.
Agents, MCP, vector & graph, engines, and storage — one self-hosted platform.