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How I Replaced Microsoft Copilot with Local AI on Raspberry Pi: A Privacy-Focused Recipe

UUnknown

2026-02-05

10 min read

A hands-on 2026 recipe for replacing cloud Copilot with a privacy-first Raspberry Pi + AI HAT+ 2 assistant—tradeoffs, steps, and governance.

Hook: Why we ditched Microsoft Copilot for a Raspberry Pi local AI

Our engineering and SRE teams were hemorrhaging time and trust. Sensitive postmortems, design notes and ticket threads were summarized by a cloud Copilot — fast, but out of our control. Compliance and data sovereignty requirements kept us awake. In 2026 we moved assistant-like workflows off the cloud and onto a small on-prem platform built on a Raspberry Pi 5 + AI HAT+ 2. The result: nearly identical day-to-day productivity for common tasks, dramatically reduced data exposure, and full control over model updates and telemetry.

Executive summary (the inverted pyramid)

What I accomplished: replaced cloud Copilot for routine assistant tasks (meeting summaries, action-item extraction, contextual code search, and ticket summarization) with a local Raspberry Pi deployment using AI HAT+ 2 and open-source stacks.

Why it matters in 2026: hardware accelerators like AI HAT+ 2 and compact, quantized models make local inference practical. Privacy regulations, client demands for on-prem data handling, and internal risk policies favor local AI. You trade some large-model polish (and explosion of automated actions) for guaranteed data sovereignty and much lower leak risk.

What you get from this guide: a pragmatic, repeatable recipe — hardware, OS and driver steps, model choices, serving stack, integrations, security practices, tradeoffs and a short case study so teams can evaluate whether an on-prem Copilot replacement fits them.

Why local AI on Raspberry Pi is realistic in 2026

By late 2025 and into 2026, two trends converged that made local Copilot-class workflows feasible on edge hardware:

Hardware accelerators designed for Raspberry Pi (notably the AI HAT+ 2) unlocked efficient on-device inference with strong quantization support.
Model engineering and GGML-style quantization techniques produced useful 3–7B parameter open models optimized for latency and memory, enabling many assistant tasks offline.

Put together, these let teams run a privacy-first assistant that handles most everyday needs without sending data to a third-party cloud.

Tradeoffs: what you gain and what you accept

Privacy and control (big wins)

Data sovereignty: Logs, embeddings and docs never leave your network unless you explicitly opt-in.
Auditability: You control model updates, telemetry, and can freeze a model version for regulatory audits.
Reduced risk surface: No external provider with access to team conversations or attachments.

Costs and limitations (realistic tradeoffs)

Model fidelity: Large cloud copilots still edge out locally hosted 70B+ models for creative or multi-step reasoning. Most everyday tasks are within reach; edge cases less so.
Maintenance: You take on OS updates, driver compatibility, and model lifecycle management.
Scaling: Raspberry Pi is great for a small team or edge nodes; for hundreds of concurrent users you’ll need clustered on-prem servers.

The recipe: step-by-step deployment

This is a practical, repeatable path for small privacy-focused teams. Adjust capacity and redundancy as you scale.

1) Plan and scope

List the Copilot features you actually use. Typical priorities: meeting summaries, follow-up items, code-context answers, ticket summaries, quick search.
Define privacy boundaries: Which data must remain on-device? Which teams can opt-in to cloud connectors?
Estimate concurrency. Raspberry Pi handles low concurrent loads; plan one Pi per team or a small cluster behind a load balancer for larger loads.

2) Hardware checklist

Raspberry Pi 5 (or current Pi with PCIe/USB 3 bandwidth).
AI HAT+ 2 (the 2025/26 accessory that provides local NN acceleration for the Pi).
Fast NVMe SSD (USB 3 or native) for models, embeddings, and logs.
Reliable power supply and a case with active cooling; the HAT and Pi can get hot under inference load.
Optional: small router or isolated VLAN for security, and an industrial UPS for availability.

3) OS and baseline security

Choose a modern Debian-based image (Raspberry Pi OS Bookworm or Ubuntu LTS) and harden it:

Apply full-disk encryption for the SSD (if you require physical-drive protection).
Disable password logins; use key-based SSH and limit SSH to management VLAN.
Install unattended-upgrades but test package updates in a staging Pi first.
Set up firewall rules (ufw or nftables) to permit only necessary ports (API and SSH for admins).

4) Drivers & accelerator firmware

Install the AI HAT+ 2 vendor drivers per their documentation (drivers matured in late 2025). Most accelerators now expose backends compatible with GGML or LocalAI runtimes.

Test with the vendor examples to confirm accelerator health and thermal throttling behavior before loading models.

5) Model selection and format

For privacy-first, offline assistants, pick models that balance capability and compute:

7B-class instruct-tuned open models (quantized to 4-bit/8-bit) are a practical sweet spot for the Pi + HAT combo.
Use GGML-compatible formats or TGI/LocalAI formats if you plan to use those runtimes.
For embeddings, use a small open model tuned for vector quality (2025–26 saw multiple efficient embedding models emerge).

Tip: keep a frozen model for compliance. When you upgrade, archive the old model binary and inference logs (encrypted) for auditability.

6) Serving stack: LocalAI (or TGI) + API gateway

Two popular, pragmatic options in 2026:

LocalAI — lightweight, supports multiple backends and is tuned for on-device inference. It exposes an OpenAI-compatible REST API that makes integration trivial.
Text Generation Inference (TGI) — better when you control high-performance GPU nodes, but usable for small local deployments with quantized models.

Recommended architecture for a Pi deployment:

LocalAI runs on the Pi, bound to 127.0.0.1 and a local port.
A small API gateway (FastAPI / Flask) handles team auth, rate-limits, audit logging, and integrates with your internal SSO (OIDC or LDAP).
UI clients connect to the gateway rather than directly to the model.

7) Embeddings and vector search

To centralize notes and make them searchable:

Compute embeddings locally and store them in a vector store. For Pi-scale, pgvector + PostgreSQL is robust and lightweight; see serverless and data-mesh patterns for sharing a Postgres vector DB across microhubs (pgvector patterns).
Use local similarity search for context retrieval on every assistant request (RAG pattern). This keeps private documents on-prem.

8) Integrations and limited connectors

Replace Copilot integrations selectively — prioritize read-only or inbound-only integration modes to preserve privacy.

Calendar and meeting summaries: run transcription locally (Whisper.cpp or other local STT) and process summaries without cloud audio upload.
Slack and Teams: use bot tokens with restricted scopes. Route messages through your gateway which strips PII if required.
GitHub: mirror required repositories or use self-hosted runners to keep sensitive code internal.

As a rule: prefer pull-based access (the Pi pulls data when requested) rather than pushing every message into the assistant.

9) Example: meeting summarizer pipeline

Record meeting audio on your local machine or meeting room device; persist to the Pi storage (encrypted).
Run local STT (e.g., Whisper.cpp optimized for the HAT) to produce a transcript.
Run a prompt that summarizes the transcript and extracts action items into structured JSON.
Store the summary and actions in your internal ticketing system (via a protected API) or as attachments in your local knowledge base.

This keeps audio and transcript never leaving your network, and provides a deterministic audit trail for compliance.

Operational guidance: monitoring, updates, and safety

Monitoring & observability

Logs: keep detailed but privacy-aware inference logs. Store request hashes, timestamps, model version and resource metrics — but not raw user content unless policy allows.
Health checks: automate health probes for CPU, HAT utilization, temperature, and model latency.
Alerting: notify when model degradation or hardware throttling occurs.

Security hardening

Network isolation: put the Pi in a dedicated VLAN with strict egress rules.
Mutual TLS or mTLS between the gateway and the Pi for service-to-service calls.
Encryption at rest for model binaries, embeddings and audit logs.
Role-based API tokens and short token lifetimes for client sessions.

Model governance

Establish an approval pipeline for model upgrades (staging → production). Freeze versions for audits — good governance parallels ideas from broader AI governance discussions.
Keep a changelog for prompt engineering changes and model parameter adjustments.
Document fallback behavior: when the Pi is offline, what assistant features are disabled?

Common challenges and mitigation

Quality gaps vs cloud copilots

Local models may hallucinate more or struggle with multi-step reasoning. Mitigations:

Compose prompts to favor extractive summarization rather than freeform generation.
Use retrieval-augmented generation (RAG) with high-quality local context to ground answers.
Surface confidence scores and provide easy escalation paths to human operators.

Scaling beyond a single Pi

For increased capacity:

Deploy multiple Pi nodes with a lightweight load balancer and a shared Postgres vector db.
For high-concurrency or large-model needs, use on-prem GPU servers running the same serving stack and reserve Pi nodes for edge/private teams.

Legal & compliance checklist

Before you cut over from cloud Copilot to local AI, confirm these items:

Data classification review — ensure the assistant is allowed to process the target data classes on-device.
Retention policy — how long do you keep transcripts, embeddings and logs? Encrypt and rotate keys.
Access control — who may query the assistant and who may access raw artifacts?
Auditability — ensure you can produce model versions, prompts and allowed transcripts for regulators.

Short case study: 20-person infra team

Context: an infra team wanted an assistant for postmortem summaries, runbook search and quick code hints without sending secrets to cloud copilots.

Implementation:

One Raspberry Pi 5 + AI HAT+ 2, LocalAI as the serving layer, Postgres+pgvector for RAG, and a simple FastAPI gateway integrated with their corporate SSO.
Workflows for meeting summaries and runbook search were implemented first; GitHub integration was read-only and limited to public repos and mirrors.

Outcomes in 90 days:

Privacy: All sensitive transcripts stayed on-prem; team security audits passed.
Efficiency: Meeting summary time reduced 40% — similar to the Copilot experience for these tasks.
Cost: One-time hardware cost and modest ops time; no per-seat cloud Copilot fees.

“For routine assistant tasks, local AI on a Pi hits the sweet spot: fast, private, and controllable.”

2026 trends & future predictions

Looking forward:

Edge accelerators will become standard: HAT-class devices will keep getting faster and more power-efficient — expect more Pi-like edge nodes in every office.
Model distillation will improve: Distilled instruction models will close the gap with large cloud copilots for most workaday tasks.
Hybrid architectures: More teams will use local-first models and fall back to vetted cloud services only for heavy jobs, preserving privacy while retaining access to advanced capabilities; read about edge-first collaboration patterns in the edge-assisted live collaboration playbook.

Also remember the cautionary trend from late 2025: desktop agents that request filesystem access (e.g., some commercial desktop AIs) had a wave of scrutiny. That underscores the need for policy and technical controls around what an assistant may access locally.

Final checklist before cutover

Confirm your model and serving stack are stable on the HAT+ 2.
Complete a security review and whitelist the Pi's network footprint.
Freeze at least one model version for audits.
Train the team on limitations and escalation paths.
Measure baseline metrics (latency, summary accuracy, error rates) to compare after cutover.

Conclusion & next steps

If your priorities are privacy, auditability and control, moving assistant-like workflows from cloud Copilots to a Raspberry Pi + AI HAT+ 2 is a practical, low-cost strategy in 2026. You’ll trade some of the highest-end generative polish for on-prem sovereignty and a predictable risk profile — a trade many security-conscious teams already prefer.

Start small: implement the meeting summarizer or runbook search first, prove value, then expand. Use the recipe above as your blueprint and iterate with clear governance.

Call to action

Ready to pilot an on-prem local AI assistant for your team? Start with a single Raspberry Pi 5 and AI HAT+ 2, or get help designing a hybrid rollout. If you want a ready-made integration path (SSO, PgVector templates, and a production-ready LocalAI gateway), schedule a demo with our deployment engineers at ChatJot — we help security-conscious teams migrate assistant workflows on-prem, keep compliance tight, and ship value fast.

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