Technical Architecture &
Specifications

A deep-dive into the Master Jurist Engine, Statutory Knowledge Graphs, and the Constitutional Context Coding™ protocol.

Proprietary Protocol

Constitutional Context Coding™

The underlying logic layer that binds generative AI outputs to rigid constitutional principles, statutory law, and judicial precedent.

1. Constitutional Framework (The Guardrails)

Every algorithmic decision is pre-validated against a rigid framework of 17 Constitutional Principles derived from Common Law.

  • Principle #1 (Claims-Based): Ensures recourse mechanisms exist.
  • Principle #8 (Data Sovereignty): Enforces GDPR and privacy rights.
  • Principle #14 (Human Sovereignty): Mandates human-in-the-loop for high-stakes decisions.
  • Principle #3 (Transparency): Requires public auditability of decision logic.

2. Statutory Knowledge Graph (The Law)

The Master Jurist references a vector-embedded graph database containing the complete legislative corpus relevant to the active sector.

  • Human Rights Act 1998: Fundamental rights impact assessment.
  • Data Protection Act 2018: Compliance with UK GDPR.
  • Public Contracts Regulations 2015: Procurement integrity.
  • Equality Act 2010: Bias and discrimination checks.

3. Case Law Precedent (The Interpretation)

Real-time cross-referencing against 36+ landmark Supreme Court judgments to ensure AI reasoning aligns with judicial interpretation.

  • Miller v Secretary of State [2017] (Constitutional limits)
  • Lloyd v Google [2021] (Data damages framework)
  • Uber BV [2021] (Employment rights definitions)
Reasoning Engine

The Master Jurist AI

Performance specifications for the core legal reasoning engine.

Knowledge Graph Integration

Structured relationship edges linking 6,345 statutes and 36 cases. Eliminates hallucinations by grounding generation in retrieved context (RAG).

Compliance Scoring

Deterministic output providing a 0-100% compliance score across four vectors: Constitutional Alignment, HRA Impact, Employment Rights, and Fiscal Probity.

Performance Metrics

Latency: Sub-5-second analysis time (95th percentile).
Throughput: Scalable to 170k+ TPS via horizontal scaling.
Confidence: 94.7% legal accuracy in benchmark tests.

Actionable Outputs

Delivers structured JSON objects containing: Decision Recommendation, Risk Factors, Specific Citations, and Remediation Steps.

System Log Example:

> ANALYZING: NHS Digital Contract (Ref: 77-A)
> TIME: 4.2s
> RESULT: APPROVED WITH CONDITIONS (Score: 92.5%)
> CITATIONS: Data Protection Act 2018 S.35; Lloyd v Google [2021]
> ACTION: Flag for DPO Review.

Process Flow

The Sovereign Wisdom Loop

A strictly defined workflow ensuring Constitutional Principle #14 (Human Sovereignty) is maintained throughout the automated governance process.

Operational Workflow

Step 1: Submission & Ingestion
Data enters the Sovereign Redactor for PII scrubbing before analysis.
Step 2: Jurist Analysis
AI engine queries the Knowledge Graph and generates a Compliance Report.
Step 3: Human Adjudication
A human officer reviews the AI advice and issues the final cryptographic signature.
Step 4: Immutable Recording
The decision (and the logic used) is hashed and written to the Bitcoin SV ledger.
Step 5: Public Audit
The transaction hash is published, allowing independent citizen verification.
Audit Infrastructure

Bitcoin SV Ledger Integration

Technical rationale for selecting Bitcoin SV (BSV) as the immutable system of record.

Unbounded Scalability

BSV supports massive block sizes, enabling the recording of national-scale government transaction volumes without network congestion.

Micro-Transaction Economics

Audit costs of <$0.001 per record ensure fiscal sustainability for long-term public sector archiving.

Immutability & Permanence

Proof-of-Work security guarantees that once a government decision is recorded, it cannot be retroactively altered or deleted by any administration.

Zero-Trust Verification

The "Don't Trust, Verify" model allows citizens to audit government performance without relying on the government's own internal databases.