# 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.