Build a Privacy-First Personal Assistant on a Pi 5 Using the AI HAT+ 2

Hook: Stop sending your life to the cloud — run a privacy-first assistant at home

If you’re a developer or IT pro worried that every voice note, calendar entry, or password hint goes off to third-party servers, you’re not alone. In 2026 the shift to on-device AI and affordable edge hardware makes it realistic to run a capable personal assistant entirely on local infrastructure. This guide walks through designing and implementing a privacy-first personal assistant on a Raspberry Pi 5 with the AI HAT+ 2 — covering model selection, secure storage, local-only APIs, and browser integration so your data never has to leave your home network.

Why build a local assistant now (2026 trends)

Since 2024 the AI landscape shifted decisively toward edge-first deployments: smaller, highly-optimized LLMs, 4-bit quantization, and vendor NPUs made capable local models feasible for single-board computers. In late 2025 and early 2026 new accessory accelerators like the AI HAT+ 2 for Raspberry Pi 5 accelerated inference performance and broadened language-model compatibility. Concurrently, privacy-first browsers and local AI runtimes (see examples such as Puma-style local browsers) validated the developer experience for in-browser local AI. The result: you can now run a practical personal assistant using open or permissive-models and keep control of the data lifecycle end-to-end.

Project overview: goals and constraints

• Goal: Build a conversational assistant on Raspberry Pi 5 + AI HAT+ 2 that processes all audio, text, embeddings, and logs locally.
• Constraints: Limited RAM (~8GB typical on Pi 5), ARM CPU, HAT accelerator with its own SDK, and power/thermal considerations.
• Security goals: Local-only network access, encrypted at-rest storage, mutual auth for client connections, minimal exposed ports.
• UX goals: Browser-based UI for local control, fast responses for common tasks, fall back to smaller models for low-latency tasks.

Hardware and software checklist

• Raspberry Pi 5 (8GB or 16GB recommended)
• AI HAT+ 2 — vendor SDK and drivers installed
• Fast NVMe SSD (USB 3.1 adapter) for swap/data (avoid SD for persistent storage)
• Microphone + speakers (USB or HAT-compatible)
• Optional: hardware security module (ATECC608A or similar) for key storage
• OS: Ubuntu 24.04 ARM64 or Raspberry Pi OS Bullseye/Bookworm depending on vendor SDK recommendations
• Container runtime: Podman or Docker (use rootless mode)

Model selection: balancing capability, latency, and privacy

Pick models that fit the Pi 5’s memory and the AI HAT+ 2’s acceleration profile. In 2026 the norm is mixed: run a small on-device LLM for low-latency dialogue and a slightly larger quantized model for complex reasoning when the accelerator allows.

Recommended configurations

• Ultra-low latency: Tiny chat model (7B → quantized Q4/Q5) using llama.cpp- or GGUF-compatible runtime. Good for quick replies, commands, and slot filling.
• Balanced: 13B quantized (Q4) with HAT offload. Suitable for multi-turn dialogues and context-sensitive tasks.
• Local embeddings: Use a small on-device encoder like a distil-sentence-transformers variant compiled for ARM. Keep embeddings local and store them encrypted.

Quantization and runtimes

Quantization is critical: 4-bit and 5-bit quantized models (Q4_0, Q4_K, Q5_K) reduce memory and improve throughput. Use LLama.cpp, ggml/gguf toolchains, or vendor-backed libraries that target AI HAT+ 2. Confirm the HAT+ 2 vendor provides an inference runtime (often accelerated with ARM NEON, SVE, or a dedicated NPU) and follow their conversion path from standard checkpoints to the HAT-optimized format.

Data architecture: private, encrypted, auditable

Design your data flows so the Pi never ships raw user data off-device. Split responsibilities:

• Short-term context: Keep conversational context in memory only and persist a compact history (last N turns) encrypted on disk.
• Long-term memory / knowledge base: Store embeddings and metadata locally in an encrypted vector store and relational DB for metadata.
• Logs & telemetry: Minimal logging by default. If you enable any telemetry for debugging, keep it opt-in and local-only.

Storage options and recommendations

• Encrypted SQLite (SQLCipher) for conversation history and small structured state. Lightweight and reliable on ARM — follow security best practices from modern storage security guides like the Security & Reliability playbook.
• Vector store: Qdrant (ARM build) or FAISS compiled for ARM can run locally. For lower overhead use a SQLite-backed vector index (Chroma-style) or an embedded FAISS index file stored encrypted.
• Binary model files: Keep model binaries on the local SSD. Use filesystem-level encryption (LUKS) if the device could be physically accessed.

API and network design: local-only by default

Make the assistant accessible from your LAN but never expose it to the public internet. Use a layered approach:

1. Bind the assistant server to 127.0.0.1 and a local LAN interface only.
2. Use mTLS for HTTPS endpoints to enforce device trust for clients (browser, phone).
3. For browser integration, prefer secure WebSocket connections with client certs or a short-lived cookie signed by the HSM.

Local-only API patterns

• Unix domain sockets: Use for inter-process communication on the same device (TTS engine ↔ assistant core). Fast and reduces network exposure.
• gRPC with mTLS: For LAN clients and multi-service deployments. Generate client certs on first setup and store the private key in an HSM or encrypted file.
• Short-lived JWTs: Issue JWTs for browser sessions via a local auth path. Sign tokens with keys protected by the HSM or a passphrase-derived key.

Example: minimal security policy

# Use firewall to restrict external access
sudo ufw default deny incoming
sudo ufw allow from 192.168.1.0/24 to any port 8443 proto tcp
# Only allow SSH from admin machines
sudo ufw allow from 192.168.1.50 to any port 22 proto tcp

Browser integration: low-friction and secure

Target browsers on the same LAN. You want a smooth UX (push-button pairing) with explicit trust. There are two practical patterns in 2026:

1) Local HTTPS + client certs

Run the web UI on the Pi over HTTPS with a self-signed CA you install on your devices during onboarding. The onboarding flow can generate a client certificate per user and store it in the browser (or OS certificate store). This gives you mutual TLS which is strong and avoids public certs.

2) WebRTC DataChannel for direct peer connection

Use WebRTC to connect the browser to the Pi without opening persistent server ports to the internet. The Pi acts as a WebRTC peer (or signaling server on the LAN), and the DataChannel carries JSON RPC messages to the assistant core. WebRTC provides encryption and NAT traversal if you decide to expose remote access later — but keep it disabled unless explicitly enabled.

UX considerations

• Provide a single-click “pair” button on the device that generates a QR code. The phone scans the code and installs the client cert or triggers WebRTC pairing.
• Offer a settings page that shows what data is stored and a one-click “wipe memory” action that deletes conversation history and embeddings.

Speech pipeline: on-device ASR and TTS

For privacy-first interaction, keep speech processing local. In 2026 lightweight ASR models and efficient streaming TTS make local voice practical:

• Use an on-device ASR (small Kaldi or whisper.cpp quantized runtime) for transcription. Whisper.cpp has ARM builds and can run in low-power config when reduced to narrowband models.
• TTS: use a lightweight neural TTS engine (e.g., VITS-derivatives or vendor TTS optimized for HAT+ 2). Keep voice personalization local and optionally encrypted.
• Pipeline: microphone → local ASR → assistant intent/memory → local TTS. No audio upload.

Secure deployment and ops

Operational hygiene matters. Treat the Pi like any security-sensitive server:

• Run services under minimal-privilege users and use systemd with Restart=on-failure. See governance patterns for micro-services in micro-apps governance.
• Keep the OS and vendor SDK updated; subscribe to vendor security bulletins for the AI HAT+ 2.
• Back up encrypted database and model files to an encrypted off-device backup (USB or NAS) with physical separation—part of an Outage-Ready approach.
• Use resource limits (cgroups) to prevent a heavy inference request from freezing the system during long runs—see advanced DevOps patterns for guidance at Advanced DevOps for Playtests.

Sample deployment stack (practical, minimal)

1. Ubuntu 24.04 ARM64 base with LUKS root partition.
2. Install AI HAT+ 2 drivers & SDK per vendor docs.
3. Podman rootless containers: assistant-core, asr-worker, tts-worker, qdrant (vector store).
4. Reverse proxy service (Caddy) bound to localhost + LAN with mTLS support.
5. systemd service to manage auto-start and healthchecks.

Developer tips and benchmarks (real-world tactics)

From real deployments in 2025–2026 the following tactics consistently improve UX:

• Hybrid model routing: Route short queries to the tiny model for instant answers and offload complex reasoning to the 13B quantized model. This reduces average latency by 30–60% — a common pattern in edge-first, cost-aware strategies.
• Context window management: Keep a rolling 2–4k token context for the small model and store long-term facts in an encrypted vector store to avoid bloating the working prompt.
• Batching & async: Batch embedding requests locally when performing similarity search to reduce CPU spikes.
• Adaptive sampling: Decrease response length for on-device replies unless the user explicitly asks for detailed answers.

Privacy checklist before you go live

• All model files and logs stored on encrypted block device
• No outbound network routes at the firewall level (drop or reject by default)
• Mutual TLS or WebRTC pairing for client access
• HSM-protected private keys or passphrase-derived keys for signing tokens
• Clear data retention UI for the end user and one-click memory erase

Example on-device flow (end-to-end)

Here’s a condensed flow for a user asking “What’s on my calendar today?”

1. User clicks the browser button (paired client) and speaks; audio captured in browser.
2. Audio sent over WebRTC DataChannel to Pi; local ASR transcribes to text.
3. Assistant checks short-term context (in-memory) and encrypted calendar store (local DB).
4. If needed, assistant computes an embedding of the transcribed query and runs a similarity search against local vector KB (Qdrant/FAISS).
5. Assistant synthesizes concise reply with on-device LLM; TTS engine renders speech and plays it locally.
6. Conversation metadata is encrypted and appended to local history; raw audio is deleted.

Problems you’ll hit and how to solve them

• Memory pressure: Use model quantization and offload parts of the model to NPU when possible. Keep an SSD for swap but tune swappiness carefully to avoid latency spikes.
• Model conversion failures: Use vendor tooling to convert weights; test conversion on a dev Pi image before production.
• Client pairing UX: Implement clear QR-code pairing and a manual fallback (one-time password) for headless setups.

Design principle: Assume the device will be offline and untrusted networks exist. Default to local-only, explicit opt-in, and auditable data handling.

Future-proofing and 2026+ predictions

Expect continued improvements in model efficiency and more middleware to ease on-device runs. By late 2026 hardware-accelerator ecosystems will converge around standard runtimes (ONNX-like acceleration for NPUs), and more open-source tools will automate quantization and conversion for ARM NPUs. For privacy-first developers, this means easier updates and safer deployments without vendor lock-in.

Actionable takeaways (implement today)

• Start with a two-model strategy: a tiny 7B quantized runtime + a 13B quantized fallback.
• Use encrypted SQLite + an ARM-compatible vector store (Qdrant/FAISS) for local memory.
• Expose the assistant only on the LAN; prefer mTLS or WebRTC pairing for browser access.
• Automate backups of encrypted data and rotate client certs regularly.
• Instrument resource limits (cgroups) and graceful degradation for intensive tasks.

Next steps and further reading

Install your AI HAT+ 2 vendor SDK and run their sample inferencing workloads. Convert one small model to the HAT format and benchmark latency/throughput. Then wire up a minimal ASR & TTS pipeline and test the pairing UX in your browser. Iterate by adding the vector store and implementing mutual TLS.

Call to action

If you want a ready-made starter repository that implements the patterns above (model conversion scripts, systemd units, encrypted SQLite schema, and WebRTC pairing examples), sign up for the webtechnoworld developer pack. Get a tested, privacy-first template that boots on Pi 5 + AI HAT+ 2 so you can go from zero to a secure local assistant in a weekend.

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