Skip to main content
Jira progress: loading…

AIIL-TI

Al - Technical Implementation

Making ZAYAZ AI Deterministic, Auditable & Compliant**

This Section describes the technical machinery that makes ZAYAZ AI safe, predictable, and fully compliant with regulatory expectations.

Where earlier parts of this manual define architecture, governance, and behavior, this part focuses on how those rules are implemented in code, enforced at runtime, and validated continuously.

The core principle guiding this section is simple:

ZAYAZ does not rely on AI “being well-behaved.”

We rely on systems that force correct behavior.

This is why this Section begins with Prompt & Policy Contracts.

Before any SRE runbooks, evaluation harnesses, or compute registry controls, ZAYAZ must ensure that:

  • every AI interaction follows a deterministic structure,
  • every answer is produced under a strict prompt contract,
  • every refusal is predictable and compliant,
  • every citation follows an auditable pattern,
  • and AI cannot operate outside the frameworks we authorize.

This is the purpose of Prompt & Policy Contracts, which forms the "runtime language" that all ZAYAZ LLMs speak.

Why Technical Implementation Starts With Prompt & Policy Contracts​

Prompts and policies are not “instructions” — they are contracts:

  • They bind the AI to the domain rules of ZAYAZ.
  • They determine what the AI may and may not use as context.
  • They prevent hallucination by forcing evidence-bounded reasoning.
  • They guarantee structured output that downstream compute and disclosure systems can rely on.
  • They integrate with the Go/No-Go engine and with jurisdiction filters.

If the prompt architecture is wrong, everything downstream — SRE detectors, validation harnesses, compute versioning, even disclosure packaging — becomes unreliable.

This is why Prompt & Policy Contracts sit at the foundation of Technical Implementation.

They define the rules that all subsequent enforcement, monitoring, and validation rely upon.

What the Technical Implementation Covers

1. Prompt & Policy Contracts​

Defines the enforceable template system for:

  • RAG grounding
  • citation patterns
  • tone and refusal rules
  • structured output schema
  • multi-framework support via registry lookups
  • safe numerical reasoning and compute delegation
  • policy enforcement (regex, contract checkers, eval harness integration)

This chapter establishes the contractual layer that ensures every AI reply is compliant and verifiable.

2. API Contracts​

Connects the AI layer to deterministic compute:

  • Compute-method registry (versioned)
  • Dataset requirements & ACLs
  • Provenance guarantees
  • Model → Compute → Validator interaction patterns
  • Operational RBAC & audit logging

This ensures AI cannot fabricate numbers and must call approved compute functions.

3. Disclosure Packaging & Exchange Protocols​

Ensures that structured AI outputs can enter:

  • XBRL/ESRS Digital Taxonomy
  • Verifier portals
  • Regulator upload channels
  • Internal audit pipelines

AI outputs become legally reportable artifacts, not raw text.

4. Extractors & Regex Specs​

Defines the validation patterns that ensure:

  • correct numeric extraction
  • structured outputs
  • compliance citations
  • evidence tagging
  • prevention of unapproved language or formats

This forms the automated “grammar” that AI must comply with.

5. Architecture Diagrams & Sequence Flows​

It’s the visual contract that binds:

  • AI behavior → retrieval, adapters, compute, packaging
  • Governance → version pinning, RBAC/ACL, audit & provenance
  • Reliability → SLO enforcement, canary paths, rollback lanes
  • Jurisdiction → EU-only routing, framework allowlists

6. SRE Runbooks​

Operational controls for:

  • index drift
  • adapter misconfiguration
  • trust-score anomalies
  • ACL enforcement failures
  • latency & refusal-quality SLOs

These are the engineering tools for safe production operations.

7. Validation & Test Harnesses​

Provides continuous evaluation of:

  • regression checks
  • citation integrity
  • refusal quality
  • numerical provenance
  • dataset consistency

This ensures ZAYAZ AI remains stable and compliant across releases.

How this Chapter Fits into the Whole ZAYAZ Model​

This is where policy becomes enforcement.

  • The General Information on AI:
    • defined what ZAYAZ is.
    • defined how AI must behave.
    • defined data models and compliance obligations.
    • ensured AI governance and lifecycle control.
    • handled stakeholder and regulator interfaces.

The "Al - Technical Implementation" turns all of this into deterministic technology.

It ensures:

  • No hallucinations
  • No unauthorized frameworks
  • No unapproved numerical reasoning
  • No unpredictable responses
  • Complete auditability
  • Full regulatory defensibility

Beginning with the most fundamental layer — Prompt & Policy Contracts — The Technical Implementation ensures that everything the AI does is grounded, controlled, and aligned with ZAYAZ’s mission of trustable AI for sustainability reporting.



GitHub RepoRequest for Change (RFC)