Phase 1: content source of truth — 40 documents across six areas

Satisfies the Phase 1 Definition of Done:

- content/ validates cleanly against the frozen schema. `make validate`
  reports OK: 40 document(s) validated, 0 violations.
- Six areas exist, each with an L1 entry: ai-overview, platform-overview,
  edge-overview, products-overview, history-overview, about-overview.
- All six areas have an L2 entry: ai-systems, platform-estate, edge-fleet,
  products-business, history-engagements, about-narrative.
- 28 L3 artefact stubs under content/<area>/l3/. Every artefacts[].url in
  every document resolves to one of them; the validator proves there are no
  dangling references (and a deliberately broken copy exits 1).
- Every metric value traces to a source line in the evidence notes or the
  approved metric set in reference/content-map.md (per-metric citations are
  recorded in the AUD-131 completion comment).
- Every document carries confidence and last_verified=2026-10-26; the
  validator rejects a missing or non-canonical value.
- docs/content-redaction.md records the redaction pass: identifiers, third
  party personal data, credentials and infrastructure detail excluded, with
  reasons, plus the two numbers that are reported rather than smoothed.

Honesty positions held: 3 businesses active (12 adoptions over time),
Where-Woof deployed and pre-market, failures kept in (edge-failures), no
promotion of in-progress work to delivered.

No site code, no templates, no CSS, no rendering (Phase 2).

content/ai/l3/ai-guardrails.md

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+---
+id: ai-guardrails
+area: ai
+level: 3
+title: "Artefact — Guardrails · hooks in the model path"
+claim: "Injection blocking and constrained tool use are code in the model path — built because the house voice channel is shared and unauthenticated."
+confidence: running
+period: "2020–"
+tech: [OmniRoute, Python]
+metrics: [{label: "tools exposed by the public assistant", value: 0}]
+artefacts: []
+public: true
+last_verified: 2026-10-26
+tags: [ai, guardrails, security, artefact]
+---
+
+# Artefact — Guardrails · hooks in the model path
+
+**Status: stub.** A redacted injection-test result lands in Phase 6, when the guardrail library is wired into this site's own assistant.
+
+## What the artefact is
+
+The hooks that run before a request reaches a provider and before a response reaches an actuator: injection patterns are blocked, tool use is constrained to a declared set, and consequential actions stop at an approval gate. Behind them, one rule that outranks every prompt — no model sits in the path of a fast or dangerous command.
+
+## What it proves
+
+Safety enforced by mechanism. A prompt instruction is a suggestion; a hook is a control.
+
+## When it is delivered
+
+The test result in Phase 6, using the same library the house runs. The policy that the public assistant has no tools at all is a Phase 5 and 6 decision and is not negotiable at runtime.

content/ai/l3/ai-hub-architecture.md

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+---
+id: ai-hub-architecture
+area: ai
+level: 3
+title: "Artefact — House AI hub service topology"
+claim: "One hub fronts the AI estate — intake · routing · pipelines · retrieval · voice output · and a single set of network tools namespaced per household member."
+confidence: running
+period: "2020–"
+tech: [n8n, Prefect, LangGraph, OmniRoute, Qdrant, MQTT, Snapcast]
+metrics: [{label: "provider gateway entry points", value: 1}]
+artefacts: []
+public: true
+last_verified: 2026-10-26
+tags: [ai, architecture, artefact]
+---
+
+# Artefact — House AI hub service topology
+
+**Status: stub.** The diagram and the generated topology dump land in Phase 7 (polish). This stub exists so the level 2 page has no dangling reference.
+
+## What the artefact is
+
+A one-page diagram of the hub: request intake (voice · chat · scheduled job), the gateway hop, the pipeline runners, the retrieval path, and the output paths (house speakers, phone alerts, dashboards). Every box is a service that runs; nothing on the diagram is aspirational.
+
+## What it proves
+
+That the estate is a designed system with a single gateway and a single set of namespaced tools — not a collection of scripts that happen to share a machine.
+
+## When it is delivered
+
+Phase 7. The source facts are in the evidence notes (sections 2 and 4). Capture must be from the live estate, so no diagram is drawn from memory in Phase 1.

content/ai/l3/ai-memory-layers.md

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+---
+id: ai-memory-layers
+area: ai
+level: 3
+title: "Artefact — Memory in two layers · Engram and Headroom"
+claim: "Memory is split by purpose — Engram stores what should persist and Headroom keeps what should not persist out of the context window."
+confidence: running
+period: "2020–"
+tech: [Engram, Headroom, SQLite, JEV]
+metrics: [{label: "memory layers", value: 2}]
+artefacts: []
+public: true
+last_verified: 2026-10-26
+tags: [ai, memory, context, artefact]
+---
+
+# Artefact — Memory in two layers · Engram and Headroom
+
+**Status: stub.** A redacted session handoff and a before-and-after context measurement land in Phase 7.
+
+## What the artefact is
+
+Engram is local-first storage for curated summaries, decisions, prompts and session handoffs in SQLite, so a conversation can be resumed in any project; it is integrated with JEV for fast compaction. Headroom is token control: it strips, cleanses and analyses code and logs before they reach the model, compressing rather than truncating.
+
+## What it proves
+
+Two distinct problems recognised as two — persistence and window economy — instead of one tool asked to do both.
+
+## When it is delivered
+
+Phase 7. Both tools are part of Sam's own workflow; they are deliberately not part of the public assistant, which is stateless by decision (see `reference/decisions.md` D11).

content/ai/l3/ai-omniroute-routing.md

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+---
+id: ai-omniroute-routing
+area: ai
+level: 3
+title: "Artefact — Model routing and cost control"
+claim: "Every model call is classified first and sent to the cheapest capable provider — six providers behind one gateway with no direct SDK calls anywhere in the estate."
+confidence: running
+period: "2020–"
+tech: [OmniRoute, JEV, DeepSeek, MiniMax, OpenRouter, Gemini, OpenAI]
+metrics: [{label: "providers in rotation", value: 6}, {label: "gateways", value: 1}]
+artefacts: []
+public: true
+last_verified: 2026-10-26
+tags: [ai, routing, cost, artefact]
+---
+
+# Artefact — Model routing and cost control
+
+**Status: stub.** The routing decision path and a redacted cost table land in Phase 7. No keys or account identifiers are ever published.
+
+## What the artefact is
+
+The decision path: request arrives, a prompt-analysis step classifies it, the gateway selects a provider and model, and the response returns with its cost recorded. Plus the comparison that demoted local inference for heavy flows — local Ollama measured slower and more expensive under the available hardware than partitioned remote calls.
+
+## What it proves
+
+Cost control implemented as routing and measurement rather than as a monthly promise, and a single chokepoint where provider access can be audited.
+
+## When it is delivered
+
+Phase 7. The evidence for the provider list and the local-versus-remote decision is in the evidence notes, section 2 and section 4.

content/ai/l3/ai-paperclip-pipelines.md

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+---
+id: ai-paperclip-pipelines
+area: ai
+level: 3
+title: "Artefact — Paperclip and n8n pipelines in production"
+claim: "Sixteen or more pipelines run real work — scanners · schedulers · project agents — with specialist agents dispatched by task and human approval gates on anything consequential."
+confidence: running
+period: "2020–"
+tech: [Paperclip, n8n, Prefect, LangGraph]
+metrics: [{label: "pipelines on n8n and Prefect and LangGraph", value: 10}, {label: "Paperclip pipelines", value: 6}]
+artefacts: []
+public: true
+last_verified: 2026-10-26
+tags: [ai, pipelines, agents, artefact]
+---
+
+# Artefact — Paperclip and n8n pipelines in production
+
+**Status: stub.** A redacted run log of one pipeline lands in Phase 7, alongside the pipeline inventory.
+
+## What the artefact is
+
+One pipeline shown end to end: trigger, steps, the agent or model each step uses, the approval gate, and the run log with identifiers removed. Alongside it, the inventory of what the sixteen or more pipelines do — email scanners, school-portal scanners, calendar scanners, project oversight, and the household automations.
+
+## What it proves
+
+Volume with operations behind it: these are scheduled, logged and monitored, not run once for a demonstration.
+
+## When it is delivered
+
+Phase 7. Counts come from the evidence notes, section 3.

content/ai/l3/ai-rag-corpus.md

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+---
+id: ai-rag-corpus
+area: ai
+level: 3
+title: "Artefact — Retrieval corpus and query graph"
+claim: "Retrieval is filtered by metadata before it searches — a tagged corpus · a four-node LangGraph query graph · and a deliberate replacement of ChromaDB with Qdrant."
+confidence: running
+period: "2020–"
+tech: [Qdrant, LangGraph, Prefect, n8n, ChromaDB]
+metrics: [{label: "retrieval stores used over time", value: 2}]
+artefacts: []
+public: true
+last_verified: 2026-10-26
+tags: [ai, retrieval, rag, artefact]
+---
+
+# Artefact — Retrieval corpus and query graph
+
+**Status: stub.** The query graph diagram and an ingest run log land later; this site's own corpus build is Phase 4.
+
+## What the artefact is
+
+The ingest and query path: documents are chunked and tagged with metadata at ingest, stored in Qdrant, and filtered on that metadata before a similarity search runs. The LangGraph graph has four nodes; the fourth decides whether the answer can be given from what was retrieved.
+
+## What it proves
+
+Retrieval designed around payload filtering and a hard stop, plus the reasoning for moving off ChromaDB — heavier footprint and weaker filtering — recorded as a decision rather than a migration.
+
+## When it is delivered
+
+The query graph diagram in Phase 7. The live demonstration of the same pattern is this site's own retrieval, delivered in Phase 4 and Phase 5.

content/ai/l3/ai-voice-pipeline.md

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+---
+id: ai-voice-pipeline
+area: ai
+level: 3
+title: "Artefact — Voice pipeline · speech in and speech out"
+claim: "Speech in on a custom wake word · local transcription · a cloned voice out · and music ducked under every announcement across eight endpoints."
+confidence: running
+period: "2020–"
+tech: [Whisper, Piper, MiniMax, ESP32, Snapcast, Mopidy, Home Assistant]
+metrics: [{label: "audio endpoints", value: 8}]
+artefacts: []
+public: true
+last_verified: 2026-10-26
+tags: [ai, voice, audio, artefact]
+---
+
+# Artefact — Voice pipeline · speech in and speech out
+
+**Status: stub.** A short recording of music ducking under a spoken announcement lands in the optional voice phase. Text describes the chain until then.
+
+## What the artefact is
+
+The chain: an ESP32 device with a microphone detects the wake word and confirms with an LED, audio is transcribed locally with Whisper, the request goes through the gateway, and the reply is spoken through Piper or the MiniMax cloned voice over all eight Snapcast endpoints, with music lowered for the announcement and restored afterwards.
+
+## What it proves
+
+Voice treated as a system front-end with latency and reliability requirements — not a party trick bolted onto a chatbot.
+
+## When it is delivered
+
+Recording in the optional voice phase (Phase 7). The endpoint count and the ducking behaviour are from the evidence notes, sections 1 and 2.