DESIGN-VINTAGE (June 26, 2026) — superseded as canonical reference. The constitution set (CONSTITUTION/OPERATING_SYSTEM/GOVERNANCE/AGENTS/TRUTH) and docs/OFFERING_MAP.md (v2.3) now govern; the technology table's "Neo4j / Graph DB" row describes the design substrate of that date — Neo4j was retired by owner decision 2026-08-09 and the live KG is Firestore-backed. Banner added by the truth-debt sweep, 2026-08-21.

MIZ OKI Enterprise Intelligence Specification

Document: MIZ OKI Enterprise Intelligence Specification Spec Version: 1.0.0 Last Updated: June 26, 2026 Status: Draft Standard — canonical reference for documentation, website, engineering spec, demo, and investor story Scope: Canonical Event Envelope · Operating Domain Model · SRPVDAL Runtime State · Decision/Action/Learning Objects · Enterprise Knowledge Graph Ontology · Autonomy Governance Framework · Intelligence Cell Standard · Connector Certification

One-line definition. MIZ OKI 3.5 converts fragmented enterprise data into governed autonomous intelligence. Every signal from every business system is normalized into a canonical intelligence envelope, mapped into a self-healing Knowledge Graph, reasoned over through the SRPVDAL loop, validated against enterprise policies, converted into explainable decisions, executed only within approved autonomy limits, and continuously improved through measured outcomes.

From Canonical Events to an Autonomous Enterprise Operating Standard

The major upgrade is this:

MIZ OKI should not treat events as records. It should treat events as evidence.

Every event should be usable by the system to answer:

• What happened?
• Who or what was involved?
• Why did it likely happen?
• What business objective does it affect?
• What policy governs it?
• What action could be taken?
• What risk exists?
• What did we predict would happen?
• What actually happened?
• What should the system learn?

That is the difference between a data pipeline and an autonomous intelligence platform.

Table of Contents

1. Core Principle
2. Corrected Platform View
3. The Universal Canonical Event Envelope
4. Canonical Event Envelope v1.0
5. Canonical ID Strategy
6. Event Categories
7. SRPVDAL State Machine
8. Intelligence Cell Standard
9. Connector Certification Levels
10. Knowledge Graph Mapping Standard
11. Business Objective Layer
12. Governance Layer
13. Autonomy Levels
14. Validation Gates
15. Decision Object
16. Action Object
17. Learning Record
18. Production Data Stores
19. Frontend Command Center
20. Enterprise Positioning
21. Public-Facing Explanation
22. Why This Is Stronger Than the Original JourneyEvent
23. Official Standard Names
24. The Critical Product Insight
25. Final Architecture Statement

1. Core Principle

The canonical model is built around a universal operating contract:

Every source emits evidence.
Every evidence object maps to entities.
Every entity belongs to a graph.
Every graph supports reasoning.
Every reasoning step produces plans.
Every plan is validated.
Every validated decision can act.
Every action is measured.
Every measurement improves learning.

This is the native logic of:

SENSE → REASON → PLAN → VALIDATE → DECIDE → ACT → LEARN

So the canonical object is not only called JourneyEvent. It becomes:

CanonicalEventEnvelope

JourneyEvent remains an advertising-specific subtype inside the broader standard.

2. Corrected Platform View

MIZ OKI is not hard-coded around five departments, seven departments, or any fixed organizational map. The platform dynamically adapts to the customer's actual enterprise structure.

A company may have:

• 5 departments
• 7 departments
• 19 departments
• 40 business units
• hybrid teams
• regional operating groups
• external vendors
• agencies
• legal entities
• subsidiaries
• franchise units

Therefore the architecture supports a dynamic OperatingDomain model:

{
  "operating_domain": {
    "domain_id": "domain_marketing_paid_media",
    "domain_name": "Paid Media",
    "parent_domain_id": "domain_marketing",
    "organization_id": "org_acme",
    "domain_type": "business_function",
    "owner": "VP Marketing",
    "kpis": ["ROAS", "CAC", "LTV", "Revenue"],
    "policies": ["paid_media_budget_policy_v3", "brand_safety_policy_v2"],
    "autonomy_level": "recommend_only"
  }
}

The platform generates the enterprise model from the customer's reality rather than forcing the customer into a static template.

3. The Universal Canonical Event Envelope

The event envelope is the single event standard across the entire system.

CanonicalEventEnvelope
│
├── envelope
├── event
├── classification
├── source
├── actor
├── identity
├── entities
├── relationships
├── business_context
├── operating_domain
├── knowledge_graph
├── causal_context
├── srpvdal
├── intelligence
├── quality
├── security
├── governance
├── observability
├── actionability
├── evaluation
├── learning
├── audit
└── raw

This envelope is the common language for every Intelligence Cell — Google Ads, Meta, OpenRTB, SendGrid, Salesforce, HubSpot, Shopify, Stripe, GA4, BigQuery, ERP, CRM, legal systems, HR platforms, finance platforms, supply chain platforms, customer support systems, product analytics systems, internal documents, emails, contracts, tickets, calls, and chat logs.

The goal is not just normalization. The goal is machine-actionable business understanding.

4. Canonical Event Envelope v1.0

{
  "envelope": {
    "schema_name": "CanonicalEventEnvelope",
    "schema_version": "1.0.0",
    "envelope_id": "env_...",
    "event_id": "evt_...",
    "created_at": "2026-06-26T12:00:00Z",
    "ingest_time": "2026-06-26T12:00:01Z",
    "environment": "production",
    "tenant_id": "tenant_...",
    "organization_id": "org_..."
  },
  "event": {
    "event_source": "google_ads",
    "event_type": "conversion",
    "event_name": "Purchase",
    "event_time": "2026-06-26T11:59:50Z",
    "event_status": "observed",
    "value": 129.99,
    "currency": "USD",
    "quantity": 1
  },
  "classification": {
    "domain": "Advertising",
    "category": "Conversion",
    "subcategory": "Purchase",
    "intent": "Commercial",
    "sensitivity": "CustomerBehavior",
    "confidence": 0.98
  },
  "source": {
    "connector_name": "google_ads_gaql_connector",
    "connector_version": "1.4.0",
    "api_version": "v18",
    "source_account_id": "1234567890",
    "source_object_type": "conversion",
    "source_object_id": "abc123",
    "source_payload_hash": "sha256_..."
  },
  "actor": {
    "user_id": null,
    "email_sha256": "sha256_...",
    "phone_sha256": null,
    "device_ifa": "ifa_...",
    "ip_hash": "sha256_...",
    "user_agent_hash": "sha256_..."
  },
  "identity": {
    "identity_id": "id_...",
    "identity_cluster_id": "cluster_...",
    "resolution_status": "resolved",
    "resolution_method": "email_hash_device_ip",
    "confidence": 0.94,
    "anonymous": false
  },
  "entities": [
    {
      "entity_type": "Campaign",
      "entity_id": "campaign_123",
      "source_entity_id": "123",
      "name": "Brand Search",
      "confidence": 1.0
    },
    {
      "entity_type": "Order",
      "entity_id": "order_A123",
      "source_entity_id": "A123",
      "confidence": 1.0
    }
  ],
  "relationships": [
    {
      "relationship_type": "BELONGS_TO",
      "source_entity_id": "evt_...",
      "target_entity_id": "campaign_123",
      "confidence": 1.0
    },
    {
      "relationship_type": "GENERATED_ORDER",
      "source_entity_id": "evt_...",
      "target_entity_id": "order_A123",
      "confidence": 1.0
    }
  ],
  "business_context": {
    "objective_id": "obj_growth_q3",
    "objective_name": "Improve profitable revenue",
    "primary_kpi": "ROAS",
    "target_value": 4.0,
    "current_value": 3.2,
    "priority": "high",
    "owner_domain": "Paid Media"
  },
  "operating_domain": {
    "domain_id": "domain_marketing_paid_media",
    "domain_name": "Paid Media",
    "parent_domain_id": "domain_marketing",
    "autonomy_level": "recommend_only"
  },
  "knowledge_graph": {
    "kg_event_node_id": "kg_evt_...",
    "kg_identity_node_id": "kg_identity_...",
    "kg_entity_node_ids": ["kg_campaign_123", "kg_order_A123"],
    "ontology_version": "mizoki_enterprise_ontology_1.0.0",
    "mapping_status": "mapped"
  },
  "causal_context": {
    "parent_event_ids": ["evt_click_1", "evt_impression_1"],
    "attribution_model": "causal_weighted_multi_touch",
    "causal_confidence": 0.82,
    "counterfactual_group_id": "cf_..."
  },
  "srpvdal": {
    "current_phase": "SENSE",
    "phase_history": [
      {
        "phase": "SENSE",
        "status": "completed",
        "started_at": "2026-06-26T12:00:01Z",
        "completed_at": "2026-06-26T12:00:02Z",
        "agent_id": "cell_google_ads",
        "outcome": "event_validated_and_persisted"
      }
    ],
    "next_phase": "REASON",
    "retry_count": 0
  },
  "intelligence": {
    "model_version": "gemini-structured-ruleset-1.0.0",
    "reasoning_engine": "GraphRAG",
    "planner": null,
    "validator": "policy_guardrail_engine",
    "decision_engine": null,
    "confidence": 0.96
  },
  "quality": {
    "schema_valid": true,
    "completeness_score": 0.97,
    "freshness_score": 0.99,
    "duplicate_probability": 0.01,
    "missing_fields": [],
    "validation_errors": []
  },
  "security": {
    "data_classification": "confidential",
    "contains_pii": true,
    "pii_fields": ["email_sha256", "device_ifa"],
    "consent_status": "allowed",
    "retention_policy": "marketing_event_retention_v1",
    "encryption_required": true
  },
  "governance": {
    "policy_version": "policy_pack_marketing_v3",
    "guardrail_version": "guardrails_enterprise_v2",
    "approval_required": false,
    "regulated_decision": false
  },
  "observability": {
    "trace_id": "trace_...",
    "span_id": "span_...",
    "workflow_id": "wf_...",
    "execution_id": "exec_...",
    "cell_id": "cell_google_ads",
    "request_id": "req_..."
  },
  "actionability": {
    "actionable": true,
    "recommended_action_types": ["budget_adjustment", "keyword_review"],
    "requires_human_approval": true,
    "risk_level": "medium"
  },
  "evaluation": {
    "prediction_id": null,
    "expected_outcome": null,
    "actual_outcome": null,
    "evaluation_status": "pending"
  },
  "learning": {
    "learning_status": "pending",
    "reward_signal": null,
    "drift_detected": false,
    "model_update_required": false
  },
  "audit": {
    "immutable": true,
    "replayable": true,
    "audit_log_id": "audit_...",
    "hash_chain": "sha256_..."
  },
  "raw": {
    "payload_ref": "gs://mizoki-raw-events/google_ads/2026/06/26/abc.json",
    "payload_hash": "sha256_..."
  }
}

This is significantly stronger than the original JourneyEvent because it supports the full autonomous loop.

5. Canonical ID Strategy

MIZ OKI needs deterministic IDs across every domain.

env_      Canonical envelope
evt_      Canonical event
ent_      Canonical entity
rel_      Canonical relationship
id_       Resolved identity
kg_       Knowledge Graph node
obj_      Business objective
pol_      Policy
dec_      Decision
act_      Action
lrn_      Learning record
wf_       Workflow
exec_     Execution
trace_    Observability trace

Deterministic hash formula:

event_id =
sha256(
  tenant_id ||
  event_source ||
  event_type ||
  source_account_id ||
  source_object_id ||
  event_time ||
  stable_business_key
)

This prevents duplicates and allows replay.

6. Event Categories

The system processes all enterprise evidence, not only marketing events.

ObservationEvent — something happened

impression · click · email opened · invoice received · contract signed · support ticket created · employee onboarded

EntityEvent — an entity changed

campaign updated · customer profile changed · vendor record updated · product price changed · account balance changed

MetricEvent — a metric moved

ROAS dropped · CAC increased · churn rose · revenue exceeded forecast · legal risk score changed

RelationshipEvent — a graph relationship was discovered or updated

customer belongs to household · vendor connected to contract · campaign influenced conversion · employee assigned to account

DecisionEvent — the system made or recommended a decision

recommend increasing budget · pause keyword · flag vendor risk · escalate contract review

ActionEvent — the system acted or proposed an action

Google Ads bid changed · email sent · workflow opened · alert created · legal hold initiated

LearningEvent — the system compared expected vs. actual outcome

predicted ROAS was 4.1, actual was 3.7 · policy blocked risky action correctly · attribution model overestimated Meta contribution · creative fatigue prediction was accurate

7. SRPVDAL State Machine

Each event moves through a formal state machine.

SENSE
  receives, validates, normalizes, enriches

REASON
  explains, connects, attributes, detects anomalies

PLAN
  creates possible actions

VALIDATE
  checks policy, finance, statistics, ethics, risk

DECIDE
  ranks options and selects recommendation/action

ACT
  executes approved action or creates human task

LEARN
  measures outcome and updates models/knowledge

Each phase emits a phase record:

{
  "phase": "VALIDATE",
  "status": "completed",
  "agent_id": "guardrail_policy_agent",
  "started_at": "2026-06-26T12:01:01Z",
  "completed_at": "2026-06-26T12:01:04Z",
  "duration_ms": 3000,
  "inputs": ["plan_123"],
  "outputs": ["validation_456"],
  "decision": "passed_with_constraints",
  "confidence": 0.91,
  "errors": []
}

This gives the platform explainability, replay, auditability, and production-grade debugging.

8. Intelligence Cell Standard

Every connector becomes an Intelligence Cell — not a data feed, but a domain-aware module that can sense, reason, plan, validate, decide, act, and learn within its scope.

Each cell has a manifest:

cell_name: Google Ads Intelligence Cell
cell_id: cell_google_ads
version: 1.0.0

primary_phase: SENSE
secondary_phases:
  - REASON
  - PLAN
  - VALIDATE
  - DECIDE
  - ACT
  - LEARN

autonomy_default: recommend_only

connectors:
  - google_ads_gaql
  - google_ads_mutate

canonical_outputs:
  - CanonicalEventEnvelope
  - GoogleAdsCampaignEvent
  - GoogleAdsSearchTermEvent
  - GoogleAdsConversionEvent
  - DecisionEvent
  - LearningEvent

knowledge_graph_nodes:
  - Customer
  - Campaign
  - AdGroup
  - Ad
  - Keyword
  - SearchTerm
  - ConversionAction
  - Budget
  - Audience
  - Geo

knowledge_graph_relationships:
  - OWNS
  - CONTAINS
  - TARGETS
  - GENERATED
  - ATTRIBUTED_TO
  - SPENT_ON
  - CONVERTED_FROM

guardrails:
  - budget_guardrail
  - brand_safety_guardrail
  - statistical_significance_guardrail
  - policy_approval_guardrail

act_permissions:
  default: disabled
  allowed_after:
    - evaluation_passed
    - policy_approved
    - human_approved

This makes every cell interoperable.

9. Connector Certification Levels

To keep third-party integrations clean, MIZ OKI introduces connector certification.

This gives MIZ OKI a partner ecosystem standard.

10. Knowledge Graph Mapping Standard

Every envelope creates or updates graph objects.

Core graph model:

(:Organization)
(:OperatingDomain)
(:Objective)
(:Policy)
(:Event)
(:Entity)
(:Identity)
(:Decision)
(:Action)
(:LearningRecord)
(:Agent)
(:Workflow)

Core relationships:

(:Event)-[:OBSERVED_FROM]->(:Source)
(:Event)-[:INVOLVES]->(:Entity)
(:Event)-[:ASSOCIATED_WITH]->(:Identity)
(:Entity)-[:BELONGS_TO]->(:OperatingDomain)
(:Event)-[:AFFECTS]->(:Objective)
(:Decision)-[:BASED_ON]->(:Event)
(:Action)-[:EXECUTES]->(:Decision)
(:LearningRecord)-[:EVALUATES]->(:Action)
(:Policy)-[:GOVERNS]->(:OperatingDomain)
(:Agent)-[:PROCESSED]->(:Event)

Advertising example:

(:Campaign)-[:CONTAINS]->(:AdGroup)
(:AdGroup)-[:CONTAINS]->(:Ad)
(:AdGroup)-[:TARGETS]->(:Keyword)
(:SearchTerm)-[:MATCHED_TO]->(:Keyword)
(:Click)-[:FOLLOWED]->(:Impression)
(:Conversion)-[:ATTRIBUTED_TO]->(:Click)
(:Campaign)-[:SPENT]->(:Budget)
(:Campaign)-[:OPTIMIZES_FOR]->(:Objective)

Legal example:

(:Contract)-[:GOVERNS]->(:Vendor)
(:Clause)-[:PART_OF]->(:Contract)
(:Risk)-[:FOUND_IN]->(:Clause)
(:Policy)-[:APPLIES_TO]->(:Contract)
(:ReviewDecision)-[:BASED_ON]->(:Risk)

Finance example:

(:Transaction)-[:POSTED_TO]->(:Account)
(:Vendor)-[:RECEIVED_PAYMENT]->(:Transaction)
(:Invoice)-[:MATCHED_TO]->(:Payment)
(:Anomaly)-[:DETECTED_IN]->(:Transaction)
(:Policy)-[:GOVERNS]->(:ExpenseCategory)

This lets MIZ OKI reason across business domains.

11. Business Objective Layer

Every meaningful event connects to business intent. Without objectives, autonomous systems optimize the wrong things.

{
  "objective": {
    "objective_id": "obj_growth_q3",
    "name": "Increase profitable customer acquisition",
    "owner": "Chief Growth Officer",
    "domain_id": "domain_marketing",
    "priority": "high",
    "kpis": [
      { "name": "ROAS", "target": 4.0, "direction": "increase" },
      { "name": "CAC", "target": 55, "direction": "decrease" },
      { "name": "LTV_CAC", "target": 3.0, "direction": "increase" }
    ],
    "constraints": [
      "monthly_budget_cap",
      "brand_safety",
      "margin_floor"
    ]
  }
}

This allows every decision to be judged against business goals.

12. Governance Layer

MIZ OKI needs a formal split between:

• Policy Decision Point (PDP) — the service that decides whether something is allowed.
• Policy Enforcement Point (PEP) — the service that blocks, permits, modifies, or escalates the action.

{
  "governance_decision": {
    "policy_id": "paid_media_budget_policy_v3",
    "decision": "allowed_with_constraints",
    "constraints": [
      "daily_budget_increase_must_be_less_than_15_percent",
      "human_approval_required_above_10000_usd"
    ],
    "reason": "Action is within budget threshold but requires margin validation",
    "approver_required": true
  }
}

This is critical for enterprise buyers — they will not allow autonomous systems to act without governance.

13. Autonomy Levels

Every domain, cell, workflow, and action type has an autonomy level.

Default is conservative: observe / recommend only.

ACT is only enabled after:

evaluation gates pass
policy approvals exist
rollback path exists
cost controls exist
audit logging exists
human override exists

14. Validation Gates

Every plan passes through validation before becoming a decision.

Example validation output:

{
  "validation_result": {
    "plan_id": "plan_123",
    "status": "passed_with_constraints",
    "overall_risk": "medium",
    "gates": {
      "financial": "pass",
      "policy": "conditional_pass",
      "statistical": "pass",
      "causal": "warning",
      "security": "pass",
      "brand": "pass"
    },
    "required_constraints": [
      "limit_budget_increase_to_10_percent",
      "monitor_for_48_hours",
      "rollback_if_cpa_increases_20_percent"
    ]
  }
}

15. Decision Object

A decision is separate from an event. Events are evidence. Decisions are conclusions.

{
  "decision": {
    "decision_id": "dec_123",
    "decision_type": "budget_adjustment",
    "status": "recommended",
    "based_on_event_ids": ["evt_1", "evt_2", "evt_3"],
    "objective_id": "obj_growth_q3",
    "recommended_action": "increase_budget",
    "target_entity_id": "campaign_123",
    "expected_impact": {
      "metric": "ROAS",
      "current": 3.2,
      "predicted": 3.8,
      "confidence": 0.78
    },
    "risk": {
      "level": "medium",
      "reason": "Recent performance is strong but sample size is moderate"
    },
    "explanation": {
      "summary": "Campaign has improved conversion efficiency over the last 7 days while maintaining stable CPA.",
      "top_evidence": ["evt_conversion_1", "evt_cost_1", "evt_search_term_1"]
    }
  }
}

This becomes the object shown in the frontend decision center.

16. Action Object

Actions are controlled, reversible, and auditable.

{
  "action": {
    "action_id": "act_123",
    "decision_id": "dec_123",
    "action_type": "google_ads_budget_update",
    "target_system": "google_ads",
    "target_entity_id": "campaign_123",
    "status": "pending_approval",
    "requested_change": {
      "field": "daily_budget",
      "from": 500,
      "to": 550,
      "currency": "USD"
    },
    "rollback_plan": {
      "rollback_action_type": "google_ads_budget_update",
      "rollback_value": 500
    },
    "approval": {
      "required": true,
      "approved_by": null,
      "approved_at": null
    }
  }
}

This prevents the platform from becoming an uncontrolled automation engine.

17. Learning Record

Learning records are where MIZ OKI becomes stronger over time — the core of LEARN.

{
  "learning_record": {
    "learning_id": "lrn_123",
    "action_id": "act_123",
    "decision_id": "dec_123",
    "objective_id": "obj_growth_q3",
    "prediction": {
      "metric": "ROAS",
      "predicted_value": 3.8,
      "confidence": 0.78
    },
    "actual": {
      "metric": "ROAS",
      "actual_value": 3.5,
      "measurement_window": "7d"
    },
    "error": {
      "absolute_error": 0.3,
      "percentage_error": 7.9
    },
    "outcome": "partially_successful",
    "model_feedback": {
      "update_required": true,
      "reason": "Prediction overstated improvement due to delayed conversion lag"
    }
  }
}

18. Production Data Stores

The same event flows into different stores for different jobs.

Recommended pattern:

Raw Source
   ↓
Connector
   ↓
CanonicalEventEnvelope
   ↓
Validation Gate
   ↓
Event Store
   ↓
Pub/Sub SRPVDAL Bus
   ↓
Knowledge Graph + BigQuery + Firestore
   ↓
Reasoning / Planning / Validation / Decision / Action / Learning

19. Frontend Command Center

The frontend exposes the same architecture visually.

Core pages:

Channel pages:

/channels/google
/channels/meta
/channels/openrtb
/channels/sendgrid
/channels/salesforce
/channels/shopify
/channels/stripe
/channels/legal
/channels/finance
/channels/hr

Each channel shows: events · entities · graph connections · metrics · decisions · actions · risks · learning outcomes.

20. Enterprise Positioning

MIZ OKI 3.5 is not another dashboard. Dashboards show what happened. MIZ OKI explains what happened, recommends what should happen next, validates the recommendation, acts when allowed, and learns from the result.

MIZ OKI is not another agent framework. Agent frameworks execute tasks. MIZ OKI governs autonomous enterprise intelligence through a full operating loop, graph memory, causal reasoning, policy validation, and continuous learning.

MIZ OKI is not another data pipeline. Data pipelines move records. MIZ OKI turns enterprise evidence into governed decisions.

21. Public-Facing Explanation

A clean explanation for the website or LinkedIn:

MIZ OKI 3.5 converts fragmented enterprise data into governed autonomous intelligence. Every signal from every business system is normalized into a canonical intelligence envelope, mapped into a self-healing Knowledge Graph, reasoned over through the SRPVDAL loop, validated against enterprise policies, converted into explainable decisions, executed only within approved autonomy limits, and continuously improved through measured outcomes.

That is the story. It is clear, business-oriented, and defensible.

22. Why This Is Stronger Than the Original JourneyEvent

The original event model solved ingestion. This expanded model solves enterprise intelligence.

Original:

Source data → normalized event → database

Expanded:

Source data
→ canonical evidence
→ identity resolution
→ Knowledge Graph
→ business objective alignment
→ causal reasoning
→ plan generation
→ governance validation
→ decision ranking
→ controlled action
→ measured learning

That is the correct MIZ OKI 3.5 architecture.

23. Official Standard Names

Use these names consistently:

24. The Critical Product Insight

The product should not ask customers:

"Which modules do you want?"

It should ask:

"What does your enterprise look like, what systems do you use, what objectives matter, what policies govern decisions, and how much autonomy do you allow?"

Then MIZ OKI generates: operating domains · Intelligence Cells · graph schema · KPIs · policies · workflows · decision queues · action permissions · learning loops.

That is the move from software implementation to enterprise operating intelligence.

25. Final Architecture Statement

MIZ OKI 3.5 is a governed autonomous enterprise intelligence platform built around the SRPVDAL operating loop: Sense, Reason, Plan, Validate, Decide, Act, and Learn.

The platform ingests signals from every enterprise system, transforms them into CanonicalEventEnvelopes, resolves identities and entities, maps them into a self-healing Knowledge Graph, reasons across business context and causal relationships, generates plans aligned to objectives, validates those plans against financial, policy, statistical, security, and operational guardrails, produces explainable decisions, executes only within approved autonomy limits, and learns continuously from measured outcomes.

Unlike static dashboards, fixed workflow tools, or isolated AI agents, MIZ OKI adapts to the customer's actual operating structure and becomes a dynamic intelligence layer across the enterprise.

This is the center of the MIZ OKI 3.5 documentation, website, engineering spec, demo, and investor story.