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AIIL-RM

Roadmap & Evolution

1. Phased Implementation​

1.1. Purpose & Role​

The following development waves will all be delivered within the first year of ZAYAZ development. Each wave represents a milestone within the program.

Its role is to:

  • Define scope boundaries for each wave.
  • Align engineering, compliance, and beta customer onboarding around a shared timeline.
  • Ensure regulator-grade auditability by making scope decisions explicit and immutable.

1.2. Wave 1 — Foundational AI Compliance​

Objective: Deliver ESRS standards Q&A with paragraph-level citations and authoritative references.

  • Scope:

    • Standards Q&A for ESRS (E1–E5, S1–S4, G1, ESRS 1/2).
    • Lookups across NACE activity mapping and IPCC/EFDB emission factors as documents.
    • No numeric computation — all numbers deferred to Computation Hub (Phase 2).

  • Technical Targets:

    • Latency SLA: p95 ≤ 1.1s for retrieval, ≤ 2.5s end-to-end with generation.
    • Data Residency: EU-only, Nordic region cloud cluster.
    • Governance: Blocker rules enforced; unsupported frameworks trigger refusal.

1.3. Wave 2 — Operational Expansion​

Objective: Expand ZAYAZ from standards Q&A to data orchestration and computation-driven reporting.

  • Scope:

    • Supplier document ingestion (Google Drive, Mail, ERP feeds).
    • Automated ingestion → validation → assurance packaging workflows.
    • Computation Hub orchestration for GHG and intensity metrics (AI–Compute Contract activated).
    • Dual-reporting mode for ESRS + ISSB interoperability.

  • Technical Targets:

    • Compute Orchestration: Secure, modular APIs (Bayesian, Monte Carlo, LCA).
    • Crosswalk Mapping: GDO-powered ESRS ↔ ISSB alignment.
    • Data Residency: Multi-region (EU + APAC) with jurisdiction routing.

1.4. Wave 3 — Global Readyness​

Objective: Make ZAYAZ a global ESG intelligence infrastructure, covering regulators, verifiers, investors, and supply chains.

  • Scope:

    • Support for SEC climate disclosure (Scope 1–3, governance, risks).
    • Integration of GRI Universal Standards.
    • Sector overlays (SASB industry standards, EU Taxonomy sector-specific KPIs).
    • Continuous assurance APIs (real-time verifier sign-off).
    • Investor ESG APIs (carbon passports, normalized metrics).
    • AI-driven scenario analytics (transition pathways, biodiversity stress, social dynamics).

  • Technical Targets:

    • Global multi-cloud deployment with regional residency enforcement.
    • Zero-Knowledge Proofs for ESG disclosures.
    • Continuous ingestion from ERP, IoT, and supply chain nodes.

1.5. Governance of Phased Scope​

  • Immutable Scope Records: Each wave scope archived in the ZAYAZ manual (AI Intelligence Layer - Appendix A).
  • Promotion Gates: Each development wave may only advance when all SLOs, validation checks, and assurance gates (AI Lifecycle & Governance) have passed, ensuring no weak link moves forward.
  • Stakeholder Alignment: Customers are onboarded with transparent visibility into current and upcoming wave scope, so expectations are set against the full one-year roadmap.
  • Regulator Assurance: The complete development program and its staged rollout are shared with regulators in advance, ensuring predictability and trust in ZAYAZ’s compliance trajectory.

2. Expansion Beyond ESRS​

2.1. Purpose & Role​

While ESRS provides the baseline for ZAYAZ in its initial rollout, the platform is designed for global applicability. Expansion beyond ESRS ensures that companies:

  • Avoid duplicate reporting by reusing disclosures across multiple frameworks.
  • Can comply with jurisdiction-specific rules (EU, US, Asia-Pacific, global).
  • Gain competitive advantage by aligning with voluntary and investor-driven frameworks (ISSB, GRI, SASB, TCFD, TNFD).

This chapter defines how ZAYAZ systematically integrates additional standards into its architecture.

2.2. ISSB Integration​

  • Scope:

    • IFRS S1 (General Sustainability Disclosures).
    • IFRS S2 (Climate-related Disclosures).

  • Implementation:

    • Crosswalks via GDO (Chapter “G”) between ESRS and ISSB nodes.
    • Shared Computation Hub modules for GHG and intensity metrics.

  • Outcome:

    • Dual reporting mode: companies produce ESRS + ISSB aligned disclosures from one dataset.
    • Reduced duplication for EU-listed firms also reporting to global investors.

2.3. SEC Climate Disclosures​

  • Scope:

    • Scope 1, 2, and 3 GHG emissions.
    • Governance, risk, and strategy disclosures.
    • Climate targets and transition plans.

  • Implementation:

    • Mapping SEC requirements into GDO as parallel nodes.
    • Specialized jurisdictional routing: SEC filings produced in XBRL/EDGAR format (Chapter “Q").

  • Outcome:

    • US-listed multinationals comply once in ZAYAZ, then route outputs to both EU and US regulators.

2.4. GRI Universal Standards​

  • Scope:

    • GRI 1 (Foundations).
    • GRI 2 (General Disclosures).
    • GRI 3 (Material Topics).

  • Implementation:

    • GDO nodes mapped to GRI disclosures alongside ESRS.
    • Disclosure packaging supports GRI-style narrative sections (e.g., stakeholder engagement).

  • Outcome:

    • Companies demonstrate compliance with the world’s most widely used voluntary framework, ensuring comparability across global peers.

2.5. SASB Industry Standards​

  • Scope:

    • Sector-specific KPIs (e.g., water intensity in textiles, methane leaks in oil & gas).

  • Implementation:

    • SASB KPIs linked to NACE activity codes (Chapter “I”).
    • Industry overlays in Computation Hub (sectoral emission intensities, water use).

  • Outcome:

    • Investors receive decision-grade ESG intelligence with sector granularity.

2.6. Voluntary & Investor-Driven Frameworks​

  • TCFD (Task Force on Climate-related Financial Disclosures)

    • Already embedded in ISSB S2, but supported explicitly for investor transparency.

  • TNFD (Task Force on Nature-related Financial Disclosures)

    • Integration of biodiversity metrics (e.g., land use, species impact).
    • Alignment with GBF (Global Biodiversity Framework).

  • SFDR (Sustainable Finance Disclosure Regulation)

    • ZAYAZ exports Principal Adverse Impact (PAI) indicators for asset managers.

2.7. Governance & Roadmap Commitments​

  • All frameworks are implemented within the one-year development program (see Chapter “T”).
  • Wave 2 → ISSB, GRI Universal Standards.
  • Wave 3 → SEC Climate Rule, SASB overlays, TNFD/SFDR voluntary disclosures.
  • All expansions tied to jurisdiction routing, assurance contracts, and regulator submissions (Chapters I–J, Q).

3. AI Maturity Path​

3.1. Purpose & Role​

The AI Maturity Path defines how ZAYAZ AI progresses from grounded Q&A to a fully compliance-assured governance AI.

Its role is to:

  • Provide a sequenced model of AI evolution within the one-year development program.
  • Ensure that each maturity step introduces new safeguards, not just new functionality.
  • Demonstrate to regulators and customers that ZAYAZ treats AI as a governed system, not a black box.

3.2. Phase A: Grounded Q&A (RAG Foundation)​

  • Scope:

    • Retrieval-Augmented Generation (RAG) over ESRS standards.
    • Paragraph-level citations for every answer.
    • Refusal on unsupported queries.

  • Governance:

    • Provenance enforcement — all outputs trace back to source nodes (See: Global Disclosure Ontology (GDO)).
    • Latency SLA (≤ 2.5s end-to-end).
    • Refusal logging for unsupported frameworks.

  • Outcome:

    • Baseline compliance Q&A engine with trusted answers + provenance.

3.3. Phase B: Behavioral Calibration (Compliance Tone)​

  • Scope:

    • Enforce compliance tone: neutral, regulator-friendly, assurance-aware.
    • Refusal quality (explain why a query cannot be answered).
    • Calibration against gold behavioral traces (See: Orchestration with Airflow DAGs).

  • Governance:

    • Evaluation Harness thresholds: refusal accuracy ≥ 99%, citation accuracy ≥ 99%.
    • Behavioral packs aligned with jurisdictional standards (EU, US, Global).

  • Outcome:

    • AI that not only answers correctly but communicates in compliance-grade style.

3.4. Phase C: Numeric Governance (AI–Compute Contract)​

  • Scope:

    • LLM no longer fabricates numbers.
    • All numeric disclosures retrieved or computed via Computation Hub (See: Domain-Specific Data Models).
    • Coverage: GHG emissions, energy intensity, water, workforce metrics.

  • Governance model:

    • AI–Compute Contract enforces numeric provenance:
      • Inputs: validated datasets (IPCC EFDB, NACE, ERP).
      • Outputs: guaranteed traceability (dataset hash + log ID).
    • Evaluation Harness checks: 0% numeric hallucination tolerance.

  • Outcome:

    • AI provides narratives + computations with provable integrity.

3.5. Phase D: Autonomous Governance Loops (Full Compliance AI)​

  • Scope:

    • AI monitors assurance coverage, regulator readiness, and disclosure completeness.
    • AI generates alerts, not just answers (e.g., “Scope 3 disclosures pending assurance”).
    • Predictive intelligence for regulatory and investor risks.

  • Governance:

  • Outcome:

    • ZAYAZ AI becomes a governance co-pilot, not just an assistant — proactively ensuring compliance readiness.

3.6. Roadmap Commitment​

  • Wave 1 (Months 3–4) → Phase A & B (Grounded Q&A + Behavioral Calibration).
  • Wave 2 (Months 5–7) → Phase C (Numeric Governance).
  • Wave 3 (Months 8–12) → Phase D (Autonomous Governance Loops).

All phases delivered within one year; sequencing provides quality assurance, not deferral.

4. Ecosystem Integration​

4.1. Purpose & Role​

The Ecosystem Integration layer ensures that ZAYAZ does not operate in isolation, but as part of a global ESG compliance and assurance network.

Its role is to:

  • Provide standardized interfaces for regulators, verifiers, investors, and supply chains.
  • Ensure data reusability — disclosures captured once can serve multiple stakeholders.
  • Maintain trust fabric across all integrations, with provenance, assurance, and governance overlays intact.

4.2. Regulator Integration​

  • Submission Interfaces::

    • EU → EFRAG XBRL/iXBRL (CSRD/ESRS).
    • US → SEC EDGAR/XBRL.
    • Global → ISSB JSON/XML taxonomies.

  • Sandbox Mode:

    • Regulators test submissions in controlled environments.
    • ZAYAZ pre-validates disclosures against schemas.

  • Outcome:

    • ZAYAZ filings are machine-readable, regulator-grade, and pre-cleared.

4.3. Verifier Integration​

  • Verifier Portals & APIs::

    • Read-only access to disclosures, evidence, and computation logs.
    • Digital Assurance Contracts for immutable sign-offs.

  • Continuous Assurance:

    • Verifiers can provide rolling sign-offs instead of annual-only reviews.

  • Outcome:

    • Assurance becomes digital, traceable, and directly connected to AI outputs — every AI-generated disclosure or narrative is anchored in verifier-signed evidence. This ensures the AI cannot drift into unverifiable territory.

4.4. Investor & Market Integration​

  • Investor APIs::

    • Normalized ESG metrics with provenance and assurance metadata.
    • Carbon passports, materiality overlays, sector benchmarking.

  • Data Feeds:

    • Integration with Bloomberg, Refinitiv, MSCI, S&P Global, and ESG rating agencies.

  • Outcome:

    • Investors receive decision-grade, comparable, and verified ESG intelligence.

4.5. Supply Chain Integration​

  • Supplier Ingestion (Wave 2–3):

    • Automatic ingestion of supplier ESG documents (Drive, Mail, ERP, APIs).
    • Structured into GDO nodes for Scope 3 disclosures.

  • Scope 3 Assurance Overlays:

    • Verifier attestations flow upward from supplier data.
    • Aggregated into Computation Hub for company-level reporting.

  • Outcome:

    • ZAYAZ provides transparent, verified Scope 3 disclosures at scale.

4.6. Governance & Trust Fabric​

  • Provenance Guarantees

    • Every ecosystem interaction (submission, verification, investor feed) anchored in disclosure → evidence → assurance chain.

  • Jurisdiction Routing

    • Different outputs tailored per jurisdiction (EU vs US vs global).

  • Auditability

    • All integrations logged with immutable IDs for regulator/auditor traceability.

4.7. Other Features​

  • Regulator–Verifier Shared Ledgers

    • Regulators access assurance logs directly.

  • Market Transparency Portals

    • Public dashboards for stakeholders beyond investors (NGOs, civil society).

  • Zero-Knowledge Proofs for Supply Chain

    • Suppliers prove compliance without exposing sensitive data.

  • Global ESG Utility Vision

    • ZAYAZ evolves into a backbone for ESG trust infrastructure across finance, assurance, and governance.

5. Strategic Evolution​

5.1. Purpose & Role​

The Strategic Evolution chapter defines how ZAYAZ will grow from an AI-powered compliance platform into the global trust infrastructure for sustainability reporting.

Its role is to:

  • Demonstrate that AI governance is not a feature, but the foundation of ZAYAZ.
  • Show how expansion beyond ESRS, AI maturity, and ecosystem integration converge into a sustainable AI governance model.
  • Position ZAYAZ as the compliance-grade AI backbone for regulators, companies, investors, and verifiers worldwide.

5.2. Trust as the Core Value Proposition​

  • Grounded Answers: Every AI-generated output is tied to a paragraph-level citation.
  • Numeric Integrity: All computations routed through the AI–Compute Contract (Domain-Specific Data Models).
  • Behavioral Calibration: Tone, refusal quality, and compliance alignment enforced.
  • Lifecycle Governance: Go/No-Go gates, evaluation harnesses, promotion/rollback workflows (Chs. K–N).

This ensures that trust in AI is equivalent to trust in disclosures.

5.3. From Compliance SaaS → Trust Infrastructure​

ZAYAZ evolves along two parallel tracks:

  • 1 - Regulatory Expansion

    • ESRS → ISSB → SEC → GRI/SASB/TNFD/SFDR (Expansion Beyond ESRS).
    • Jurisdiction routing ensures correct filings in EU, US, and global markets.

  • 2 - AI Governance Deepening

    • From RAG and refusal handling → to full AI–Compute Contracts → to autonomous compliance loops.
    • Every step strengthens AI’s alignment with regulator-grade assurance.

Together, these tracks transform ZAYAZ into infrastructure, not just software — a backbone for AI-governed sustainability reporting.

5.4. Stakeholder Integration as AI Checkpoints​

Each stakeholder interface is also an AI governance checkpoint:

  • Dashboards (Link...) → Allow internal teams to validate AI answers, disclosures, and citations in real time.
  • Verifier Interfaces (Link...) → Anchor AI outputs in digital assurance contracts.
  • Regulator Submissions (Link...) → Convert AI-driven disclosures into machine-readable, regulator-validated filings.
  • Investor Feeds (Link...) → Ensure AI intelligence entering markets is traceable, comparable, and verified.
  • Governance Overlays (Link...) → Apply provenance, access control, and assurance coverage across all outputs.

This ecosystem means that AI cannot operate outside a governed trust perimeter.

5.5. Future Strategic Directions​

  • Zero-Knowledge Proofs for AI Outputs

  • Regulators and investors verify compliance without needing raw data access.

  • Federated Assurance Models

  • Distributed verifier participation; multiple assurance providers cover different sections of disclosures.

  • Continuous Compliance

  • From annual filing → to continuous assurance, continuous reporting, continuous AI governance.

  • Global ESG Utility

  • ZAYAZ becomes not just a SaaS platform but the global trust fabric for AI-powered ESG disclosures, linking regulators, companies, and capital markets.

5.6. Conclusion​

ZAYAZ demonstrates that AI in sustainability reporting can be both powerful and trustworthy. By embedding AI governance into every layer — data ingestion, RAG, behavioral calibration, numeric computation, assurance packaging, lifecycle management, and ecosystem interfaces — ZAYAZ creates a new category: Compliance-Grade AI. As global disclosure requirements expand, ZAYAZ will scale in scope. But its mission remains unchanged: to make AI not just usable, but governable, in the most compliance-critical reporting domain of our time.

Note: For detailed internal policies, validation logs, and SOPs governing AI lifecycle operations, see Appendixes A "Internal AIMS & Gov".



GitHub RepoRequest for Change (RFC)