Vision Paper: A Shared Semantic Layer
The Problem
Modern analytical systems repeatedly encode business meaning independently across dashboards, reports, APIs, AI assistants, alerts, and automation. Every consumer reconstructs semantics from physical schemas, creating duplicated logic, inconsistent interpretations, and increasing maintenance cost as the number of consumers grows.
This problem compounds with AI-driven data interfaces. AI is the first consumer class that must resolve business meaning on the fly in response to open-ended natural-language questions, rather than consuming pre-built reports. AI exposes this semantic gap; it doesn't create it.
Existing Approaches & Limitations
| Approach | Representative Tools | Philosophical Assumption | Limitations in Local/Edge Environments |
|---|
| Vendor BI Models | Power BI, Microsoft Fabric | Centralized, warehouse-bound | Round-trip latencies, proprietary lock-in |
| Declarative Models | LookML | Centralized query targets | Monolithic server execution |
| Transformation Layers | dbt Semantic Layer, MetricFlow | Metric-first batch transformation | High server latency for real-time edge UI |
| Semantic Servers | Cube.js, AtScale | Centralized OLAP servers | Assumes server in the loop for every interaction |
Core Principles
Design Goals
• Local-First & Edge-Executable: Resolvable in the client without server round trips.
• Deterministic: The same request against the same ontology version yields identical queries every time.
• AI-Native: Designed from the start to serve open-ended natural language intent.
• Human-Authorable: Domain experts can declare business concepts without understanding compiler internals.
Non-Goals
• Not replacing data warehouses or query engines.
• Not a general-purpose graph database.
• Not an ETL or batch data transformation framework.