Scroll to follow the story — from the chaos of real-world data to a unified, agent-driven database for the AI era.
Heterogeneous Data Sources
01 — The Problem
Heterogeneous, unstructured, schema-less
Databases were designed for structured, schema-conforming data. But reality looks different — scientific datasets, enterprise records, medical histories, sensor streams. Each exists in its own format and semantic world. Moving and transforming data (ETL) is not enough. We need a layer that captures what data means.
02 — Semantic Layer
A Semantic Knowledge Graph as the foundation
We represent heterogeneous data through a Semantic Knowledge Graph — a unified structure that captures not just data, but its meaning and relationships. Unlike ETL pipelines that transform data, the graph expresses intent: what entities exist, how they relate, and what they represent across modalities.
03 — Agents
Autonomous construction and maintenance
Constructing and maintaining a knowledge graph over ever-changing, heterogeneous data is beyond human capacity. Autonomous agents continuously traverse, analyze, and update the semantic layer — detecting changes, resolving conflicts, and extending coverage as new data arrives.
04 — Query Layer
Full query engine for semantic data
Atop the semantic graph sits a full query engine — parsing, planning, and executing queries across richly structured, multi-modal data. Agents issue queries on behalf of users and applications, navigating the full complexity of the knowledge layer.
05 — The Full Vision
Semantic Operators & Query Optimization
Some queries must reason over unstructured content — classifying text, matching images, extracting relations. For these, we embed LLMs directly into query execution as Semantic Operators. Since LLM calls are expensive, query optimization — proxy models and cardinality estimation — is critical to making the system practical at scale.