The Village: Sovereign Community AI in Production

What Is the Village

The Village is a member-owned platform for whānau, marae, clubs, and community organisations. Each community gets its own isolated tenant with sovereign data storage, AI-assisted features, and governance-protected privacy. The platform supports te reo Māori throughout.

All AI processing runs on the platform's own infrastructure — a locally fine-tuned Llama model with no data sent to external AI providers. Communities operate with full data ownership and can withdraw consent at any time.

Deployment Facts

Duration: 18+ months in production
Tenant Model: Multi-tenant (multiple communities)
AI Model: Sovereign Llama (QLoRA fine-tuned)
AI Features: 4 governed features live
Infrastructure: NZ + EU (no US dependency)

Sovereign AI Architecture

The Village runs its own language model — not an API call to a US hyperscaler, but a locally fine-tuned model where the training data, model weights, and inference pipeline all remain under community control.

Local Language Model

Llama 3.1 8B and Llama 3.2 3B, fine-tuned with QLoRA on community-specific data. All inference runs on the platform's own GPU infrastructure.

Sovereign Infrastructure

Production servers in New Zealand and the EU. No data transits US jurisdiction. Community data never leaves the deployment it belongs to.

Community-Controlled Training

QLoRA fine-tuning on domain-specific data with consent tracking and provenance. Communities can withdraw training data and trigger model retraining.

For a detailed account of the model architecture, training approach, and governance integration, see Village AI / SLL: Sovereign Locally-Trained Language Model.

Polycentric Governance

The distinctive contribution of the Village is its governance architecture. Rather than a single operator making all decisions, the platform implements polycentric governance — multiple co-equal authorities that share structural control over how AI is used.

Co-Equal Authority

Communities maintain architectural co-governance — not just consultation rights, but structural authority over how their data is used. Drawn from te ao Māori concepts of rangatiratanga (self-determination) and kaitiakitanga (guardianship).

Right of Non-Participation

Members can opt out of any AI feature without losing access to the platform. AI governance defers to human judgment on values questions and never overrides community decisions.

Taonga-Centred Design

Cultural treasures (taonga) are governed as first-class objects with provenance tracking, withdrawal rights, and community authority over how they appear in AI contexts.

Tenant-Scoped Isolation

Each community operates in complete data isolation. No cross-tenant data sharing. Each tenant's governance decisions apply only within their own boundary.

The research foundation is described in Taonga-Centred Steering Governance: Polycentric AI for Indigenous Data Sovereignty.

How Governance Works in Practice

When a member uses any AI feature, the request passes through six governance checks before a response is generated. Each check is independent and can block or modify the request.

1

Member request received

A member asks for help, requests OCR, or uses story assistance.
2

Values boundary check

Is this a values question that requires human judgment? If so, the AI defers rather than answering.
3

Intent validation

Does the request conflict with stored governance rules or attempt prompt injection? Cross-references against known instruction sets.
4

Context and session health

Is the session within acceptable bounds? Monitors for context pressure and triggers graceful handoff when needed.
5

Permission-filtered retrieval and response

The sovereign Llama model generates a response using RAG context filtered by the member's permissions. All processing stays on-infrastructure.
6

Scope verification

Is the response appropriate to what was asked? Detects scope creep and blocks responses that exceed the original request.
7

Delivery with attribution

Response delivered to the member with source attribution. Every step is logged for audit.

What the Platform Delivers

Help Centre

Members ask questions in natural language and get answers drawn from help content, stories, and documentation — filtered by their permissions.

Governance: Values boundary check prevents AI from making judgments; intent validation blocks prompt injection attempts.

Document OCR

Upload a document and get the text extracted automatically. Useful for digitising letters, certificates, and historical records.

Governance: Requires explicit consent before processing. All operations are audit-logged with full provenance.

Story Assistance

AI-assisted writing suggestions for community stories and family histories. Helps with structure, prompts, and gentle editing.

Governance: Values boundary check prevents inappropriate content suggestions; scope verification ensures the AI stays within what was asked.

AI Memory Transparency

Members can see, edit, and delete what the AI "remembers" about them. Full audit dashboard shows every AI interaction.

Governance: Multi-stakeholder consent required. Persistence decisions classified and auditable. Members control their own data.

Honest Limitations

This case study documents preliminary evidence from a production multi-tenant deployment. We are transparent about the following limitations:

Small Scale: The Village currently serves a small number of community tenants. Generalisability to larger deployments or different community types is unknown.
Self-Reported Metrics: No independent verification of logged data has been conducted.
Operator-Developer Overlap: Framework developer also operates the Village (conflict of interest).
Limited Adversarial Testing: No formal red-team evaluation has been conducted.
Voluntary Invocation: AI could theoretically bypass governance if not configured to use it.

What This Demonstrates

Evidence Supports

• Sovereign AI deployment is technically feasible for small community organisations
• Polycentric governance can operate in production without prohibitive overhead
• Multi-tenant isolation with per-community governance is achievable
• Governance violations are detectable and auditable
• The framework learns from failures (documented incident responses)

Evidence Does NOT Support

• Framework effectiveness at scale (thousands of concurrent users)
• Generalisability across different AI systems or model architectures
• Resistance to sophisticated adversarial attacks
• Regulatory sufficiency (EU AI Act compliance untested)