Introduction
Most consumer “smart assistants” are not assistants but cloud terminals. Audio, video, and context are captured locally, while intelligence and decision-making happen elsewhere. This architecture is convenient, but incompatible with privacy, ownership, and deep personalization. This article series explores a different approach: a private, owner-controlled AI assistant running locally on Raspberry Pi 5 (16 GB RAM). The system recognizes specific people, reacts with personalized behavior, communicates naturally, retrieves information, and integrates smart functionality, while keeping perception and identity on the edge.
Edge AI vs Cloud Assistants
Cloud-first assistants offer massive compute and rapid iteration, but suffer from permanent data exfiltration, limited identity isolation, vendor lock-in, and unverifiable behavior. An edge-first assistant trades unlimited scale for determinism, privacy, offline capability, and full control. This project adopts a hybrid edge philosophy: perception, identity, memory, and automation are local; external APIs are used selectively for language and information retrieval.
System Boundaries
Clear boundaries prevent scope creep.
The assistant IS:
– single-owner
– identity-aware
– event-driven
– locally perceptive (vision + audio)
– modular and inspectable
The assistant IS NOT:
– a surveillance system
– a multi-user platform
– a cloud mirror
– an autonomous agent with open permissions
– a replacement for personal devices
High-Level Architecture
The system is composed of five cooperating engines:
Vision Engine → detects presence
Identity Engine → resolves who is present
Scenario Engine → selects behavior
Dialogue Engine → communicates
Automation/Services → executes actions
Each module is loosely coupled and replaceable.
Vision Engine
The Vision Engine answers a narrow question: is a known person present? It does not perform continuous recording or remote streaming. The owner uploads reference photos; face embeddings are generated and stored locally. Matching is event-based. Raw images are not retained. This is recognition, not surveillance.
Identity Engine
Identity is central. There is exactly one Owner identity. The owner configures the system, approves known individuals, defines scenarios, controls API access, and owns all data. Other people are recognized but have no permissions. They are mapped only to scenarios and responses. This mirrors a root/user separation model.
Scenario Engine
A scenario defines how the assistant reacts to a specific identity or event. It includes greeting style, voice tone, optional notifications, and automation triggers. Examples: “Welcome home, John. You received an email an hour ago.” or “Hello Uncle Alan. Good to see you.” Scenarios are explicit, inspectable, and reversible.
Dialogue Engine
Conversation is handled by a controlled dialogue engine. Speech is converted to text, enriched with identity context, filtered through scope rules, and routed either locally or via selected APIs. The assistant does not decide what it is allowed to do; it only responds within predefined limits. This prevents accidental autonomy.
Why Raspberry Pi 5 (16 GB)
Raspberry Pi 5 finally makes serious home Edge AI practical. It can run face recognition pipelines, vector databases, audio processing, and multiple concurrent services reliably. The 16 GB RAM variant provides headroom for embeddings, buffers, and future expansion. This is not excess—it is engineering margin.
Privacy and Ethics by Design
Privacy is enforced architecturally, not by policy. Cameras and microphones process locally. No silent uploads. No background analytics. Physical indicators are recommended. Trust emerges from predictable, inspectable behavior.
What Comes Next
This article defined the architectural foundation and constraints. The next article covers preparing Raspberry Pi 5 for Edge AI: OS selection, performance tuning, storage, cooling, and security hardening. Step by step, the series will lead to a fully functional personal AI assistant that remains under the owner’s control.
