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+# ADR 0002: Hybrid AI Architecture for SCADA Platform
+
+**Status:** Accepted
+**Date:** 2026-06-19
+
+## Context
+
+The SCADA platform needs to support both real-time local decision making on edge devices (Raspberry Pi) and complex reasoning / long-term analysis in the cloud. A single model size cannot efficiently satisfy both requirements while keeping egress costs low.
+
+## Decision
+
+We will use a **hybrid AI architecture**:
+
+- **Edge (small models, 1B–9B parameters)**: Run on Raspberry Pi for local signal classification, simple alert triage, OCR/needle gauge interpretation, and immediate control decisions.
+- **Cloud (larger models, 30B+ or frontier models)**: Handle complex reasoning, anomaly detection across time, user intent understanding, report generation, and high-level planning.
+- A small "triage" model on the edge decides whether to escalate a situation to the cloud model or handle it locally.
+
+## Consequences
+
+- Lower ongoing data egress costs (only rich context is sent during alerts or when requested).
+- Better responsiveness for time-critical local events.
+- Increased system complexity (model routing, context handoff, versioning).
+- Requires careful design of the handoff protocol between edge and cloud models.
+
+## Alternatives Considered
+
+- Pure cloud architecture: Rejected due to high egress costs and latency on poor cellular connections.
+- Pure on-device large models: Rejected due to hardware constraints on Raspberry Pi and inability to handle very complex reasoning.
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