OBA Core — AI Workforce Intelligence Engine

Developed by Horquva · MVP Demo · Sunrise Care (Fictional Company)

OBA Core (Organizational Brain Analysis) is an enterprise-grade intelligence engine that automatically discovers, maps, and analyzes every AI agent operating inside an organization. It answers the three questions no organization can currently answer:

• Who owns each AI agent?
• What breaks — and how badly — if one fails?
• What happens to the organization if a key person leaves?

OBA Core answers all of this in seconds, with full risk scoring, cascade simulation, and prioritized action plans.

"The only thing that matters: This is actually useful." — Horquva

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Table of Contents

• The Problem We Solve
• What Was Built
• System at a Glance — How Everything Fits Together
• Intelligence Modules — Phase 1 (Modules 01–20)
• Architecture Layers (Phase 3 — Ontology · Relationship · Reasoning · Truth · Context · Voice)
• Executive, Network & Prediction Intelligence (Modules 21–35)
• Constitutional Intelligence, Automation & Meta-Brain (Modules 36–55)
• Constitutional Runtime — Organizational Brain (backend/brain/)
• Demo Results Summary
• How to Run
• Project Structure
• Full Tech Stack
• Module Engineering
• Phase 6 — Constitutional Intelligence & Meta-Brain (Master Registry M01–M55, LOCKED)

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The Problem We Solve

Organizations are deploying AI agents faster than they can govern them. The result is invisible risk:

• Agents running with no owner, no documentation, no backup
• One person quietly controlling 5+ critical agents — with zero coverage
• Nobody knowing which agent failure cascades into a full department breakdown
• Leadership making decisions with no visibility into their AI infrastructure

OBA Core makes the invisible visible.

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What Was Built

OBA Core is a full-stack intelligence platform with three layers:

Layer	Technology	Purpose
Intelligence Engine	Python · uv · rich	55 analytical modules (Master Registry M01–M55, LOCKED) that process org data
Backend API	Node.js · Express · Supabase	REST API serving all intelligence data
Executive Dashboard	Next.js 16 · TypeScript · Tailwind · Recharts	Interactive visualization for leadership

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System at a Glance — How Everything Fits Together

Read this section first. It explains the entire OBA Core system in plain language — what it is, how a question travels through it, who builds each part, and the vocabulary used everywhere else in this document. Everything after this section is detail.

The big idea

Every modern organization runs on a hidden web of people, AI agents, tools, workflows, and knowledge. When one node fails — a key person leaves, an agent breaks, a tool goes offline — the damage cascades in ways nobody can see in advance. OBA Core turns that invisible web into a living map and reasons on top of it, so leadership can ask plain questions ("What are our biggest risks?", "What breaks if Robert leaves?") and get verified, prioritized answers in seconds.

It does this with 55 constitutional intelligence modules (M01–M55) that do not run as 55 disconnected scripts. They boot together as one Organizational Brain: a shared knowledge graph, a common language, and a runtime that discovers, orders, and fuses every module's output into a single executive answer.

How a single question flows through the system

1. A question enters — from an executive, the dashboard, or an API call (e.g. "What are our biggest organizational risks?").
2. The Knowledge Graph is the ground truth — every person, system, AI agent, tool, and workflow exists exactly once, with all relationships mapped.
3. Capability discovery — the runtime finds which modules can answer, instead of hard-wiring calls between them.
4. Dependency ordering — modules are sorted into a constitutional execution order so each one runs after the intelligence it depends on.
5. Modules execute — each returns a standard Intelligence Package: a result, a confidence score, supporting evidence, and recommended actions.
6. Truth gate — the Truth module (M46) verifies findings; the Autonomous Advisor (M48) is not allowed to recommend on unverified truth.
7. The Meta-Brain fuses everything — the Orchestrator (M55) always runs last, merging every module's intelligence into one prioritized executive answer with a single fused confidence.

            Executive question
                   │
                   ▼
        ┌───────────────────────┐
        │   Knowledge Graph     │  entities + relationships (one shared truth)
        └───────────┬───────────┘
                   ▼
        Capability discovery  →  which modules can answer?
                   │
                   ▼
        Dependency ordering   →  constitutional run order
                   │
                   ▼
        55 modules execute    →  result + confidence + evidence each
                   │
                   ▼
        Truth (M46) gate      →  advice withheld unless verified
                   │
                   ▼
        Meta-Brain (M55)      →  fuses everything, runs LAST
                   │
                   ▼
         Single executive answer  (fused confidence + recommendations)

The four intelligence layers — who builds what

The 55 modules are owned by four engineers, each responsible for one layer of the Brain:

Layer	Lead engineer	Modules	What it delivers
Knowledge Platform	Huzaifa (13)	M01, M02, M03, M07, M08, M19, M20, M22, M28, M29, M31, M34, M35	Discovery + memory: registries, entities, relationships, the Knowledge Graph and ontology — the shared truth every other module reads from
Brain Runtime & Core Reasoning	Kamran (21)	M04, M05, M06, M09, M10, M14, M18, M24, M25, M26, M27, M30, M36, M38, M39, M40, M46, M48, M50, M54, M55	The engine that boots the Brain, routes every call, orders modules, enforces the constitutional rules, and fuses all intelligence via the Meta-Brain
Prediction, Learning & Org Science	Tahir (14)	M11, M12, M13, M17, M32, M33, M37, M41, M42, M43, M44, M45, M47, M49	Looks forward and inward: predictive risk, forecasting, patterns, culture, maturity, benchmarks, continuous learning and the organizational digital twin
Executive Experience & Autonomous Ops	Anusha (7)	M15, M16, M21, M23, M51, M52, M53	The executive-facing surface + autonomy: verification, workflow orchestration, avatar & briefings, and self-healing / governance / continuity automation

Supporting teams

Area	Team	Responsibility
Backend infrastructure	Fizza & Shawal	Node.js + Express API and Supabase persistence; the routes that serve all intelligence to the dashboard
Executive dashboard (frontend)	Frontend team	Next.js 16 + TypeScript + Tailwind + Recharts visualization for leadership
Architecture, integration & review	Kamran (Technical Lead)	Owns the Brain runtime and integrates every engineer's work into one constitutional source of truth

Key concepts (glossary)

Term	Meaning
Module (M01–M55)	A single unit of organizational intelligence. The registry is LOCKED — no renaming, merging or duplication
Capability	The named service a module exposes (e.g. m03.risk.intelligence) so it can be discovered rather than hard-referenced
Knowledge Graph	The Brain's long-term memory: every entity and every relationship, each stored exactly once
Entity / Relationship	The nodes and edges of the graph. Relationships are first-class — no dangling edges allowed
Intelligence Package	The standard envelope every module returns: type, payload, confidence, evidence, recommendations
Constitutional rules	Non-negotiable runtime laws: discovery before execution; Truth (M46) gates the Advisor (M48); the Meta-Brain (M55) always runs last
Boot Report	The acceptance report printed when the Brain boots — modules discovered, per-owner counts, and every criterion check
Confidence	A 0–1 score each module attaches to its output; the Meta-Brain fuses these into one figure

By the numbers

Metric	Value
Constitutional modules	55 (M01–M55, LOCKED)
Engineering owners	4 — Huzaifa 13 · Kamran 21 · Tahir 14 · Anusha 7
Runtime files (backend/brain/)	21 JavaScript modules
Seeded demo graph	16 entities · 24 relationships
Registered capabilities	55
Stub responses	0 — every module computes real graph-derived intelligence

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Intelligence Modules — Phase 1 (Modules 01–20)

Module 01 — Ownership Intelligence

Analyzes every AI agent across the organization and scores ownership risk.

What it does:

• Identifies the primary owner and backup owner for each agent
• Detects fully orphaned agents (no owner assigned whatsoever)
• Flags owner concentration risk — one person controlling too many critical agents
• Calculates a risk level per agent: LOW / MEDIUM / HIGH / CRITICAL

Risk Scoring Formula:

Factor	Points Added
No owner assigned	+40
No backup owner	+30
Not documented	+15
Agent criticality: critical	+15
Agent criticality: high	+10
Agent criticality: medium	+5

Score → Risk Tier: < 20 = LOW · 20–39 = MEDIUM · 40–69 = HIGH · 70+ = CRITICAL

Sunrise Care findings:

• Robert owns 5 agents — zero backups — highest single-owner concentration in the org
• 2 agents fully orphaned: Inventory Agent, Data Backup Agent
• 9 of 15 agents have no backup owner

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Module 02 — Dependency Intelligence

Builds a full dependency graph of all AI agents and maps cascade failure paths.

What it does:

• Constructs a directed dependency graph: which agents feed into which
• Detects Single Points of Failure (SPOF) — agents whose failure breaks 3+ downstream agents
• Simulates cascade failure: if Agent X goes down, which agents are affected?
• Calculates upstream depth — how deep in a dependency chain each agent sits

Sunrise Care findings:

• 4 Single Points of Failure identified across 15 agents
• 6 agents have 3 or more downstream cascade victims
• Onboarding Agent failure → 4 agents immediately break
• Inventory Agent failure → 4 agents immediately break

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Module 03 — Risk Intelligence

Fuses ownership risk and dependency data into a single composite risk score per agent, then computes the Organizational Health Score.

What it does:

• Combines Module 01 + Module 02 outputs into one unified risk score
• Applies CRITICAL override rule: any orphaned agent OR any SPOF with no backup = CRITICAL regardless of score
• Calculates the Organizational Health Score (0–100) — a single number representing how well-governed the organization's AI infrastructure is
• Produces a complete risk breakdown per agent for executive review

Sunrise Care findings:

• 5 agents at CRITICAL risk
• 6 agents at HIGH risk
• Organizational Health Score: 56/100 — AT RISK

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Module 04 — Recommendation Engine

Generates specific, named, prioritized actions based on every risk finding — not generic advice.

What it does:

• Reads every risk finding from Module 03 for every agent
• Generates a targeted recommendation per risk: names the agent, names the person, names the exact action
• Prioritizes all recommendations: CRITICAL → HIGH → MEDIUM, then Quick wins first
• Produces a Top 5 Most Urgent Actions list for immediate leadership action
• Calculates how each fix improves the Organizational Health Score

Sunrise Care findings:

• 12 actionable recommendations generated
• Top priority: immediately assign owners to Inventory Agent and Data Backup Agent
• Redistribute Robert's 5 agents — single departure would orphan all of them
• Recovery plan provided with projected Health Score improvement per action

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Module 05 — What-If Simulation Engine

Simulates every possible disruption scenario and calculates its exact impact on organizational health before it happens.

What it does:

• Simulates every owner leaving the organization (one by one)
• Simulates every CRITICAL/HIGH/SPOF agent failing
• Recalculates the Organizational Health Score for each scenario in real time
• Shows before → after risk level for every affected agent
• Ranks all scenarios from most dangerous to least — so leadership knows exactly where fragility lives

Simulation logic:

• Person Leaves → their agents lose primary ownership (+35 risk each), Health Score recalculated
• Agent Fails → failed agent reaches maximum risk (score 170), all cascade victims receive +30 risk penalty

Sunrise Care findings:

• Worst scenario: Robert leaves → Health Score collapses from 56 → 49
• 5 agents become immediately unmanaged if Robert is unavailable
• Worst agent scenario: Onboarding Agent failure drops Health Score to 47
• Every scenario ranked so leadership can prioritize risk mitigation investment

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Module 06 — Human-Agent Dependency Map

Maps every person in the organization to the agents they control and scores human-level coverage risk.

What it does:

• Builds a complete ownership tree per person: which agents they own, at what risk level
• Calculates a coverage score per person: what % of their agents have backup owners
• Identifies Human SPOFs: individuals who own 3+ agents with no backup coverage anywhere
• Lists every coverage gap across the organization with exact agent names

Sunrise Care findings:

• Robert = Human SPOF — 5 agents owned, 0% coverage, all CRITICAL or HIGH risk
• Sarah = 100% coverage — all 3 of her agents have backup owners
• 9 total coverage gaps identified across the organization
• 7 agents have a primary owner but zero backup coverage

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Module 07 — AI Tool Intelligence

Audits every AI tool in use across the organization — usage, risk, dependencies, and financial exposure.

What it does:

• Scores every AI tool for risk: ChatGPT, Claude, Gemini, Microsoft Copilot, GitHub Copilot
• Maps tool-to-agent and tool-to-workflow dependencies: if this tool goes offline, what breaks?
• Identifies tools with no backup/alternative and no usage policy
• Shows department-level exposure per tool
• Calculates total monthly AI tool spend across the organization

Sunrise Care findings:

• ChatGPT = CRITICAL — 7 users, 4 departments, powers 3 agents, no policy, no backup
• Microsoft Copilot = HIGH — 8 users across all 8 departments, no backup alternative
• 3 of 5 tools have no fallback option assigned
• If ChatGPT access is revoked: Lead Generation, Marketing Campaign, and Customer Support workflows all break simultaneously
• Total monthly AI tool spend: $1,444

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Module 08 — Workflow Intelligence

Maps every business workflow step by step — Human → Tool → Agent → Outcome — and scores failure risk at each node.

What it does:

• Visualizes every workflow as a full sequential chain with named actors at each step
• Scores each workflow for risk: ownership gaps, undocumented status, human SPOF dependency
• Identifies single-node failure points — the one person or tool whose removal collapses the entire workflow
• Surfaces workflows with no runbook, no backup owner, and no recovery path

Sunrise Care findings:

• 2 CRITICAL workflows: Lead Generation (Robert, no backup, undocumented) and IT Operations (David, no backup, undocumented)
• All 7 workflows have exactly one human dependency — no workflow survives its owner leaving
• 14 single-node failure points identified across all workflows
• 3 workflows have zero documentation: Lead Generation, IT Operations, Analytics Reporting

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Module 09 — Knowledge Risk Intelligence

Maps where critical organizational knowledge is stored — in people's heads — and calculates what disappears if they leave.

What it does:

• Calculates a Knowledge Concentration Score per person (0–100%)
• Identifies sole knowledge holders: people who are the only ones who know how a critical asset works
• Lists every undocumented agent, workflow, and AI tool across the organization
• Maps exactly which assets are unrecoverable if a specific person leaves today
• Surfaces knowledge gaps: assets with no documentation AND no backup owner

Sunrise Care findings:

• Robert = CRITICAL knowledge concentration (100%) — sole owner of 5 agents + 1 workflow, all undocumented
• Mike and Lisa = HIGH concentration risk (64% and 54%)
• 13 total undocumented assets across agents, workflows, and tools
• If Robert leaves today: 6 assets are permanently unrecoverable with no documentation and no backup

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Module 10 — Organizational Memory Intelligence

Tracks the institutional memory preservation status of every AI asset and calculates how much organizational knowledge would survive a major personnel disruption.

What it does:

• Assigns a memory status to every asset: PRESERVED / AT RISK / VULNERABLE / LOST
• Calculates the Institutional Memory Health Score™ (0–100)
• Identifies critical memory carriers — individuals who are the sole holders of undocumented knowledge
• Flags assets classified as LOST: no owner, no documentation, no recovery path

Memory Status Definitions:

Status	Meaning
PRESERVED	Documented + backup owner exists
AT RISK	Has backup but lacks documentation
VULNERABLE	Has documentation but no backup owner
LOST	No owner, no documentation — unrecoverable

Sunrise Care findings:

• PRESERVED: 14 assets · VULNERABLE: 10 assets · AT RISK: 1 asset · LOST: 2 assets
• Robert = CRITICAL memory carrier — sole holder of 7 assets, 6 of which are undocumented
• David = HIGH risk — sole carrier of IT Operations Workflow + both IT tools, all undocumented
• Institutional Memory Health Score: 54/100 — AT RISK

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Module 11 — Predictive Risk Intelligence

Predicts which agents are likely to escalate to high/critical risk in the near future and surfaces emerging threats before they happen — an explainable, weighted ML-style risk model.

What it does:

• Scores every agent on a forward-looking risk model using current criticality, dependency exposure, owner/backup coverage, AI-tool (platform) health, and critical-workflow membership
• Classifies each agent's predicted threat: LOW / MEDIUM / HIGH / CRITICAL
• Flags emerging threats — agents not critical today but predicted to become critical
• Gives a plain-English reason list per agent for full explainability

Sunrise Care findings:

• 4 agents predicted at CRITICAL threat, 2 at HIGH, 8 at MEDIUM
• Heavy dependency exposure + missing backups are the top escalation drivers
• Orphaned agents (Inventory, Data Backup) carry the highest predicted risk

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Module 12 — Organizational Forecasting Intelligence

Forecasts the future state of the organization across Health, Memory, and Continuity, and projects a 30 / 60 / 90 day outlook.

What it does:

• Health Forecast — will the agent + tool ecosystem degrade?
• Memory Forecast — risk of losing institutional knowledge when key owners leave (no backup / no docs)
• Continuity Forecast — can workflows keep running under stress (criticality-weighted resilience)?
• Rolls the three forecasts into a single 90-Day Organizational Outlook

Sunrise Care findings:

• 90-Day Organizational Outlook: 52/100 — AT RISK
• Memory is the weakest dimension — knowledge concentrated on a few owners with no backup
• Several critical workflows are fragile (no backup owner, undocumented)

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Module 13 — Human-AI Collaboration Intelligence

Analyzes the human side of the AI ecosystem — adoption, dependency concentration, and how effective human-agent pairing really is.

What it does:

• AI Adoption Score — how broadly the workforce engages with AI tools
• Human Dependency Score — whether individuals are over-relied-upon (too many critical agents/workflows on one person)
• Collaboration Score — effectiveness of human-agent pairing (documented + backed-up ownership)
• Surfaces the people at highest dependency risk and departments with weak AI coverage

Sunrise Care findings:

• AI Adoption: 100/100 — every named staff member uses at least one AI tool
• Human Dependency: 54/100 — dangerous concentration (Robert carries the most critical load)
• Collaboration: 40/100 — most agents still lack documentation or a backup owner

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Module 14 — Decision Intelligence

Reconstructs the key organizational decisions encoded in the data, builds a decision trail for each, and scores how sound each decision was — answering why a decision was made, what influenced it, and was it the right call.

What it does:

• Treats every ownership assignment, tool adoption, and workflow setup as an explicit decision and rebuilds its decision trail (the reasoning chain that led to it)
• Surfaces the influences behind each decision: criticality, owner concentration, backup coverage, documentation, fallback availability
• Scores Decision Quality per decision: GOOD / ACCEPTABLE / POOR / HARMFUL
• Computes a single org-wide Decision Quality Index (0–100) so leadership can see whether the org's past decisions are sound, mixed, or weak
• Generates a targeted fix for every poor or harmful decision

Decision Quality Scoring (start 100, penalties applied):

Factor	Penalty
Asset left with no owner (orphaned)	−65 (ownership) / −50 (workflow)
No backup owner chosen	−25
Deployed without documentation / runbook	−15 to −20
Owner already concentrates 5+ agents	−20
Critical asset/tool/workflow with no backup or fallback	−15 to −20
Critical tool adopted with no fallback selected	−30

Score → Quality Tier: 80+ = GOOD · 55–79 = ACCEPTABLE · 30–54 = POOR · < 30 = HARMFUL

Sunrise Care findings:

• 27 organizational decisions audited across ownership, tooling, and workflows
• 3 HARMFUL decisions — all assigning a critical agent to Robert with zero backup (Lead Scoring, Lead Qualification, Billing)
• 8 POOR decisions, including adopting ChatGPT as a critical tool with no fallback and leaving Inventory + Data Backup agents unassigned
• Decision Quality Index: 67/100 — MIXED

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Module 15 — Verification Intelligence

Tracks and verifies every action taken across the organization — by humans, AI agents, or tools — and flags actions that violate ownership or policy rules.

What it does:

• Logs every action taken in every workflow with actor type, actor name, and outcome
• Verifies whether each action is policy compliant and properly accountable
• Flags actions performed by known single points of failure (e.g. unbacked owners)
• Produces a full verification record with status: COMPLETED / FLAGGED / FAILED / PENDING

Sunrise Care findings:

• 36 total actions verified across 7 workflows
• 2 actions flagged — both performed by Robert, due to zero backup coverage
• 2 policy violations identified
• 0 unverified actions

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Module 16 — Workflow Orchestration Intelligence

Determines the next step in every workflow, assigns it to the correct actor, and detects collisions where multiple workflows compete for the same human, agent, or tool.

What it does:

• Tracks current step and total steps for every active workflow
• Identifies the next actor (human, agent, or tool) responsible for the next step
• Detects collisions — cases where the same actor is required by 2+ workflows simultaneously
• Flags workflows as BLOCKED when a collision risk is detected

Sunrise Care findings:

• 7 workflows orchestrated
• 17 collisions detected — including ChatGPT shared across 3 workflows, Microsoft Copilot overloaded across 5 workflows, and Lisa required by 2 workflows simultaneously
• All 7 workflows currently flagged BLOCKED due to unresolved collisions

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Module 17 — Organizational Learning Intelligence

Enables the system to learn from the organization's current state — failure patterns, decision follow-through, and incident exposure — and scores overall learning maturity.

What it does:

• Learn from Failures — identifies failure-prone assets (undocumented + unbacked + critical) and likely repeat offenders
• Learn from Decisions — measures how many known risks are still unmitigated (critical assets without backup / docs)
• Learn from Incidents — ranks departments by incident exposure (weakest documentation + backup coverage)
• Computes an overall Learning Maturity Score

Sunrise Care findings:

• Learning Maturity: 40/100 — EARLY STAGE
• Several critical assets show repeatable weakness patterns (undocumented + no backup)
• Departments with the lowest documentation + backup coverage are the most incident-prone

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Module 18 — Organizational Continuity Intelligence

Scores every asset on its ability to survive a major disruption (a key person leaving or a tool going offline), identifies exactly what must be protected, and produces concrete continuity plans — answering what survives, what fails, and what must be protected.

What it does:

• Assigns a Continuity Score (0–100) to every agent, workflow, and AI tool — the likelihood it survives a major disruption
• Classifies each asset: SURVIVES / DEGRADED / FAILS / LOST
• Builds a Continuity Risk Map at the department level — average continuity and at-risk counts per department
• Identifies what must be protected: critical/high assets that would Fail or be Lost
• Generates a Continuity Plan per must-protect asset (assign owner, name backup, document + store runbook, select fallback)
• Computes the org-wide Organizational Continuity Score (0–100)

Continuity Status Definitions:

Status	Meaning
SURVIVES	Owned, backed up, and documented — recoverable
DEGRADED	Partial coverage — survives but with disruption
FAILS	Missing backup or documentation — does not survive cleanly
LOST	No owner, no backup, no documentation — unrecoverable

Sunrise Care findings:

• 27 assets assessed — 12 SURVIVES · 3 DEGRADED · 10 FAILS · 2 LOST
• 2 assets classified LOST: Data Backup Agent (0/100) and Inventory Agent (2/100)
• IT department is the most fragile — 18/100 average continuity
• 10 critical/high assets flagged as must be protected, each with a generated continuity plan
• Organizational Continuity Score: 63/100 — AT RISK

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Module 19 — Governance Intelligence

Scores how well every asset is governed — owner accountability, documentation, and policy coverage — and builds a department-level governance heatmap with gap detection.

What it does:

• Assesses each entity (agent, tool, workflow) for governance: owner assigned, documented, policy coverage, policy freshness
• Scores each entity 0—100 and classifies it HEALTHY / WARNING / AT RISK / CRITICAL
• Builds a governance heatmap and detects governance gaps ranked by severity
• Computes an org-wide Governance Score

Sunrise Care findings:

• Governance Score: 47/100 — AT RISK
• No formal governance policies cover most assets — ownership and documentation gaps dominate
• High-criticality, undocumented, unbacked assets are the worst governance offenders

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Module 20 — Accountability Intelligence

Builds RACI-style accountability links for every asset — who is Responsible, Accountable, Consulted, Informed — maps responsibility chains, and scores accountability coverage.

What it does:

• Derives accountability links (Responsible / Accountable / Consulted / Informed / Decision Authority) for each entity
• Builds responsibility chains and detects weak structures (e.g. same person Responsible and Accountable)
• Scores each entity and computes an org-wide Accountability Score

Sunrise Care findings:

• Accountability Score: 76/100 — WARNING
• 13 entities carry accountability links, but 7 have the same person Responsible and Accountable — no separation of duties
• Only 4 unique people appear across all responsibility chains — heavy concentration

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Intelligence Platform Foundation (Phase 2)

The shared data + intelligence backbone that powers the Governance and Accountability modules and standardizes how every pillar reads organizational data.

What it provides:

• Organizational Data Models — typed entities for agents, tools, workflows, policies, accountability links, and governance gaps
• Intelligence Pipeline — normalizes raw org data into a single comparable entity surface and derives policies + accountability links
• Governance Data Framework — reusable governance scoring, heatmap, and gap-detection logic
• Intelligence Storage Layer — persists pillar analyses and a queryable intelligence index

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Organizational Intelligence Engine — Five Pillars Integration (Phase 2)

The platform foundation that connects every individual module into one unified system. Instead of reading 14 separate reports, leadership gets a single integrated view built on three layers — Intelligence Logic (shared signals derived from every asset), Intelligence Relationships (how one weakness drags others down), and Intelligence Scoring (one comparable score per dimension) — rolled up across the Five Pillars.

The Five Pillars:

Pillar	Code	What it measures
Domain Intelligence	DI	Is the organization mapped — ownership, dependencies, documented domain?
Memory Intelligence	MI	What knowledge is retained vs. trapped in one person's head?
Operational Intelligence	OI	Can day-to-day operations absorb a person or tool going down?
Organizational Continuity Intelligence	OCI	What survives a major disruption (criticality-weighted)?
Governance Intelligence	GI	Accountability and compliance — owners, backups, documentation?

How it works:

• Intelligence Logic — flattens agents, workflows, and AI tools into one comparable asset surface and derives shared signals (ownership, backup, documentation, dependency and criticality coverage)
• Intelligence Scoring — scores each pillar 0–100 (STRONG / MODERATE / WEAK / CRITICAL) and rolls them into one Organizational Intelligence Score
• Intelligence Relationships — flags when a weak pillar drags another (e.g. weak Memory → weaker Continuity), so leadership fixes the root cause instead of the symptom

Rating bands: 80+ = STRONG · 60–79 = MODERATE · 40–59 = WEAK · < 40 = CRITICAL

Sunrise Care findings:

Pillar	Score	Rating
Domain Intelligence (DI)	81/100	STRONG
Memory Intelligence (MI)	53/100	WEAK
Operational Intelligence (OI)	41/100	WEAK
Organizational Continuity Intelligence (OCI)	56/100	WEAK
Governance Intelligence (GI)	55/100	WEAK

• The domain is well-mapped (ownership on 25/27 assets), but Memory, Operational, Continuity and Governance are all weak — knowledge lives in people's heads, not documents
• 4 dragging relationships detected — MI→OCI, OI→OCI, GI→DI, GI→OCI — weak governance and memory are the root cause pulling continuity down
• Organizational Intelligence Score: 57/100 — WEAK

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Architecture Layers (Phase 3 — Ontology · Relationship · Reasoning · Truth · Context · Voice)

These six architecture layers turn 20 independent modules into one coherent Organizational Brain. Until now every module could form its own opinion about the same entity, which created overlap and contradiction (e.g. Dependency and Accountability both reasoning about ownership). With Phase 3, modules no longer make decisions — they generate signals, and a single Truth Layer reconciles those signals into one authoritative answer with confidence, evidence, and freshness.

Ontology  →  Relationship  →  Modules emit signals  →  Reasoning  →  Truth  →  Context + Voice
(what exists) (how connected)    (20 perspectives)        (insight)   (one truth)  (executive + voice access)

Architecture Layer A1 — Ontology Layer — Defines what exists

The formal vocabulary of the Organizational Brain. Every entity is registered here, under a defined type, before any module is allowed to reference it. This guarantees all 20 modules talk about the same entities in the same language.

• Entity types defined: Human · Team · AI Agent · System · Workflow · Knowledge
• Relationship vocabulary defined: owns · depends_on · governs · collaborates_with
• Tacit (undocumented) knowledge is promoted into explicit Knowledge entities so it becomes visible and trackable.

Sunrise Care: 55 entities registered across 6 types — 8 Human, 9 Team, 15 AI Agent, 5 System, 7 Workflow, 11 Knowledge.

Architecture Layer A2 — Relationship Layer — Defines how everything connects

The graph the Brain navigates when reasoning about the organization. It maps every connection between entities using the ontology's relationship vocabulary, then surfaces the most connected nodes (hubs) — the structural pressure points.

Relationship	Count
owns	25
depends_on	43
governs	14
collaborates_with	56

Sunrise Care: 138 relationships mapped. Biggest hubs — Robert (29 connections), Lisa (28), Sarah (24) — confirming structural over-concentration around a few people.

Architecture Layer A3 — Reasoning Layer — Turns signals into understanding

Raw module signals are just facts ("no backup", "undocumented"). The Reasoning Layer connects related signals into insight — the so what and the why — with an explicit reasoning chain, so leadership sees conclusions, not just data points.

• Detects patterns: knowledge concentration, single-point-of-failure cascades, compound risk (undocumented and no backup), and systemic documentation gaps.
• Every insight carries a step-by-step reasoning chain and the evidence behind it.

Sunrise Care: 16 insights generated (9 CRITICAL, 7 HIGH). Top conclusion — Robert is a structural single point of failure, reasoned from ownership + documentation + backup signals.

Architecture Layer A4 — Truth Layer — One organizational truth

The authority layer. Every module's view of an entity arrives as a signal; the Truth Layer combines them, resolves disagreements, and produces one determined truth per entity — each carrying:

• Confidence — how strongly the modules agree (disagreement caps confidence).
• Evidence — the full signal trail behind every verdict (auditability).
• Freshness — Fresh / Aging / Stale, based on documentation and backup coverage.

Sunrise Care findings:

Metric	Result
Entities reconciled	27
Signals combined	135
CRITICAL truths determined	4
HIGH truths determined	9
Contradictions resolved into a single truth	18
Trust Score (avg confidence across the Brain)	75%

• Example reconciliation — Inventory Agent: Risk/Knowledge/Continuity signals say HIGH, but Ownership/Dependency say LOW → Truth Layer resolves to HIGH at 60% confidence and flags the contradiction with its full evidence trail, instead of letting modules silently disagree.

Architecture Layer A5 — Context Intelligence Layer — Real-time executive context

Packages live organizational context per scope (department/team) so every Executive Avatar interaction is situationally aware instead of generic. Each package carries the scope's assets, owners, tools, and active risk items.

Sunrise Care: 9 context packages built across 22 assets, surfacing 10 active risk items. Highest-pressure scope right now: Sales.

Architecture Layer A6 — Voice Agent Context Layer — Semantic foundation for voice

The layer that lets a Voice Agent understand which entity a person means (entity + alias resolution) and answer organizational questions in natural language, grounded in the ontology.

Sunrise Care: 35 voice-resolvable entities with 76 name aliases mapped, and 9 ready-to-answer intents — e.g. "Who owns the Lead Scoring Agent?" → resolves the entity → "Robert — and there is no backup owner, so it is a single point of failure."

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Executive, Network & Prediction Intelligence (Modules 21–35)

These modules extend the 20 core modules into executive-facing, network-science, and prediction territory. Every module is documented in strict sequence — 21 through 35 here, then 36 through 55 in the next section — so nothing is missing. They run on the extended organizational dataset (history, incidents, decisions, external entities, and knowledge areas) and are exposed through the backend API. Each module names its lead engineer; the modules that ship inside the pure-Python package horquva_modules_py/ (Tahir, M32–M49) are marked accordingly.

Module 21 — Executive Avatar Intelligence

A single executive-facing persona that answers leadership questions directly from the Organizational Brain, instead of making executives read 20 separate reports.

What it does:

• Accepts plain leadership questions ("What is my biggest risk?", "Who is overloaded?")
• Pulls the answer live from ownership, risk, and continuity signals
• Always names the specific entity and person behind each answer
• Acts as the conversational front-door to every other module

Sunrise Care findings:

• Biggest risk surfaced: a critical agent with no backup owner
• Most overloaded person: Robert (heaviest ownership concentration)
• 4 executive questions answered directly from live data

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Module 22 — Voice Intelligence Engine

Turns spoken questions into answers by classifying intent and resolving the entity against the ontology.

What it does:

• Classifies each spoken question into an intent: ownership / risk / status / general
• Resolves which entity the speaker means (e.g. "the Payroll Agent")
• Returns a natural-language answer grounded in real data
• Produces a short spoken daily summary for voice playback

Sunrise Care findings:

• 4 voice queries resolved across ownership, risk, and status intents
• Example: "Is the Payroll Agent a risk?" → "Yes — owned by Lisa, no backup, undocumented."

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Module 23 — Executive Briefing Intelligence

Auto-generates the daily "top things to know" by pulling the most important signal from across the whole Brain.

What it does:

• Surfaces the top unresolved single points of failure
• Highlights the most overloaded owner
• Reports the latest incident and its lesson
• Tracks the documentation trend over time and flags pending decisions

Sunrise Care findings:

• 5-point executive briefing generated automatically
• Documentation trend: 28% → 35% over 4 months (still below safe levels)
• Flags critical no-backup agents as the #1 item every day

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Module 24 — Decision Support Intelligence

Turns raw risk findings into a prioritized "what to do next" queue with a transparent scoring model, and reviews how past decisions turned out.

What it does:

• Converts every risk (single points of failure, active incidents, undocumented critical knowledge) into a concrete decision
• Scores each decision 0–100 using impact × urgency ÷ effort, and boosts anything sitting on a dependency blast-radius
• Groups the queue by driver so leadership sees why each action is on the list
• Reviews the decision log for choices that went negative, mixed, or are still pending, and flags them for revisit
• Gives leadership a ranked action list instead of a wall of risks

Sunrise Care findings:

• 25 prioritized decisions queued — 10 single-point-of-failure, 9 undocumented-knowledge, 6 active-incident
• Top action: assign a backup owner to the Lead Scoring Agent (highest impact × urgency)
• 3 past decisions flagged for revisit

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Module 25 — Organizational Health Intelligence

A single weighted composite health index across five resilience dimensions, broken down by department, with a trend direction from historical snapshots.

What it does:

• Scores five dimensions: Documentation (20%), Continuity/backups (25%), Ownership spread (15%), Critical safety (25%), Incident load (15%)
• Combines them into one weighted Organizational Health Index (0–100)
• Breaks health down per department so leadership sees exactly where the weakness lives
• Uses the monthly history (risk-index time series) to determine whether things are improving or declining
• Classifies overall state: CRITICAL / WARNING / STABLE

Sunrise Care findings:

• Organizational Health Index: 28/100 — CRITICAL
• Trend: improving (risk index falling month over month)
• Weakest dimension: Critical safety (0/100) — critical assets with no backup coverage
• Weakest department: Finance

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Module 26 — Executive Memory Intelligence

Remembers what leadership should not forget — recurring patterns, lessons from past incidents, and decisions that went wrong.

What it does:

• Detects recurring incident patterns (the same failure type happening again)
• Flags repeat-offender entities that appear in multiple incidents (chronic weak points)
• Surfaces the lesson attached to every critical/high incident
• Flags decisions that turned out negative — especially irreversible ones
• Identifies "hero dependency" — one person repeatedly resolving incidents
• Ranks everything by relevance so the most important memory sits on top

Sunrise Care findings:

• 7 memory items surfaced from 6 recorded incidents (1 recurring pattern, 5 lessons, 1 bad decision)
• Recurring outages flagged as a pattern, not one-offs
• Hero-risk and chronic weak points surfaced for leadership follow-up

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Module 27 — Executive Context Intelligence

Ranks "what matters right now" so leaders focus on the most urgent context first.

What it does:

• Pulls open incidents, critical SPOFs, pending decisions, dependency blast radius, and declining metrics into one feed
• Scores each item by urgency: CRITICAL / HIGH / MEDIUM / LOW
• Raises urgency for single points of failure that feed multiple downstream dependencies
• Sorts the feed so the most pressing context is always on top
• Gives the Executive Avatar its situational awareness

Sunrise Care findings:

• 19 context items ranked by urgency — 10 single-point-of-failure, 6 incidents, 2 weak metrics, 1 pending decision
• Highest live urgency: CRITICAL — unbacked critical assets rank at the top of the "what matters now" feed

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Module 28 — Universal Dependency Graph

Builds one dependency graph across the entire organization — agents, tools, workflows and people — not just agent-to-agent links.

What it does:

• Connects every entity type into a single directed graph
• Computes each node's blast radius (how many things depend on it)
• Finds the longest dependency chain in the organization
• Becomes the shared graph that Modules 34 and 35 reason over

Sunrise Care findings:

• 39 nodes connected by 58 dependency edges
• Longest dependency chain: 6 hops deep
• Highest blast-radius nodes are the most dangerous to lose

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Module 29 — Organizational Relationship Intelligence

Scores the health of every ownership/backup relationship, not just whether it exists.

What it does:

• Rates each relationship 0–100 based on backup presence and documentation
• Classifies each as healthy / at risk / fragile
• Counts fragile, single-link relationships that would break on one departure
• Tracks reciprocal backup links between people

Sunrise Care findings:

• Average relationship strength: 72/100
• 9 fragile relationships (single owner, no backup or docs)

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Module 30 — Knowledge Concentration Intelligence

Pinpoints where critical knowledge is dangerously concentrated in too few people, using both a bus factor and a Herfindahl-Hirschman concentration index (HHI).

What it does:

• Measures how much critical knowledge each person holds (knowledge areas + owned critical assets)
• Calculates the organization's bus factor (how few people hold 50% of critical knowledge)
• Computes the HHI concentration index (0–10000) and classifies it HEALTHY / MODERATE / HIGH / SEVERE
• Reports the share held by the single most critical person
• Flags critical knowledge areas with only a single holder, plus undocumented critical areas
• Breaks concentration down per person (critical items held + how many are undocumented)

Sunrise Care findings:

• Concentration level: SEVERE (HHI 2850/10000)
• Bus factor: 2 — losing 2 people removes half of critical knowledge
• Top person holds 40% of all critical knowledge
• 4 critical knowledge areas have a single holder: Lead Scoring Logic, Payroll Rules, CRM Integration, Backup & Recovery

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Module 31 — Organizational Ecosystem Intelligence

Maps the full ecosystem — internal tools plus external vendors and platforms — and measures external dependency exposure.

What it does:

• Links external entities (OpenAI, Anthropic, GitHub, Supabase, Stripe, Slack) to the internal assets that rely on them
• Counts how many internal assets each external entity ultimately supports
• Flags critical external dependencies that have no alternative
• Surfaces external single points of failure outside the company's control

Sunrise Care findings:

• 6 external entities mapped against 5 internal tools
• 3 critical external dependencies identified

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Module 32 — Dependency Impact Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Simulates a failure at any node and walks the dependency graph (breadth-first, with impact decay) to reveal the full cascade blast radius, then ranks the organization's true single points of failure.

What it does:

• Injects a failure at any agent, tool, workflow, or person and propagates it through the dependency graph
• Applies an impact-decay factor at each hop so nearer victims count more than distant ones
• Aggregates the total blast radius per origin node
• Ranks every node to expose the organization's real single points of failure

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Module 33 — Dependency Evolution Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Diffs dependency snapshots over time to show how coupling is growing or shrinking, and tracks whether the organization is getting more or less fragile.

What it does:

• Compares two dependency snapshots and diffs added / removed edges
• Tracks how each node's coupling has evolved between snapshots
• Flags whether overall fragility is trending up or down
• Gives leadership an early read on structural drift

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Module 34 — Hidden Dependency Intelligence

Surfaces indirect couplings that no single module can see on its own.

What it does:

• Detects transitive dependencies (A → B → C means A silently depends on C)
• Finds shared-resource coupling (assets sharing the same tool fail together)
• Finds shared-owner coupling (assets joined only through one person)
• Exposes second-order risk that looks safe in any single view

Sunrise Care findings:

• 18 hidden dependencies discovered across transitive, shared-resource, and shared-owner types
• Reveals couplings that ownership or dependency views alone would miss

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Module 35 — Organizational Network Intelligence

Applies network science to reveal who actually holds the organization together and where it bottlenecks.

What it does:

• Builds the people network from shared tools and backup relationships
• Computes centrality to find the most connected people
• Identifies the primary bottleneck through whom information flows
• Flags weakly-connected or isolated people

Sunrise Care findings:

• Primary bottleneck (highest centrality): Robert
• Confirms structural over-reliance on a small core of people

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Constitutional Intelligence, Automation & Meta-Brain (Modules 36–55)

From here the engine moves from analysis into constitutional intelligence, deeper prediction, organizational science, and governed automation. The sequence continues unbroken — 36 through 55, nothing skipped. Modules marked ships in horquva_modules_py/ run as a self-contained, pure-Python package (python3 horquva_modules_py/demo.py, no external dependencies); the constitutional modules (Kamran) run via uv run main.py and are exposed under /api/intelligence/*; the automation modules (Anusha) run through the backend API. Two constitutional rules are enforced here: Truth (M46) gates the Advisor (M48), and the Meta-Brain Orchestrator (M55) always runs last.

Module 36 — Signal Intelligence

Engineer: Kamran · GET /api/intelligence/signals

An early-warning system that fuses ownership, dependency, incident, and metric signals into a single organizational stability score and surfaces the active warning signals leadership should act on before they escalate.

What it does:

• Collects weak signals from across every layer (ownership, dependency, incidents, declining metrics)
• Computes an organizational stability score
• Ranks active signals by how close they are to becoming a real problem
• Feeds the constitutional layer with a verified early-warning feed

Module 37 — Pattern Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Detects recurring patterns across incidents and behavior, and classifies how regular each pattern is using the coefficient of variation (regular vs. sporadic).

What it does:

• Scans the incident and activity history for repeating event types and sequences
• Measures how regular each pattern is using the coefficient of variation (tight cadence vs. random spikes)
• Classifies every pattern as REGULAR / PERIODIC / SPORADIC
• Flags the recurring patterns most likely to strike again so leadership can pre-empt them

Module 38 — Opportunity Intelligence

Engineer: Kamran · GET /api/intelligence/opportunities

Turns risk findings inside-out into a prioritized opportunity backlog — the highest-leverage improvements and quick wins that raise organizational health the fastest.

What it does:

• Converts every open risk into a concrete improvement opportunity
• Scores each opportunity by leverage (health gained vs. effort required)
• Separates quick wins from strategic bets
• Gives leadership a ranked "where to invest next" backlog

Module 39 — Capability Intelligence

Engineer: Kamran · GET /api/intelligence/capability

Scores the organization's operating capability per department (ownership depth, documentation, backup coverage, tooling) to show where the org is strong and where it is thin.

What it does:

• Scores each department's operating capability across ownership depth, documentation, backup coverage, and tooling
• Rolls the four dimensions into a single capability index per department
• Ranks departments from strongest to thinnest so leadership sees where capacity is real vs. fragile
• Flags capability gaps that need hiring, cross-training, or documentation

Module 40 — Strategic Alignment Intelligence

Engineer: Kamran · GET /api/intelligence/alignment

Measures how well day-to-day operations line up with stated priorities, computes an alignment index, and flags the areas that are drifting out of alignment.

What it does:

• Compares where effort and ownership actually sit against the organization's stated priorities
• Computes an alignment index (0–100) showing how tightly execution matches strategy
• Flags misaligned areas — critical priorities with thin coverage, or effort spent on low-priority work
• Gives leadership a clear "are we working on the right things?" read

Module 41 — Organizational DNA Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Builds the organization's "DNA profile" across six dimensions (e.g. autonomy, documentation, resilience) — a fingerprint of how the organization actually operates.

What it does:

• Profiles the organization across six behavioral dimensions (autonomy, documentation, resilience, collaboration, ownership, adaptability)
• Builds a single "DNA fingerprint" that captures how the org actually operates, not how it claims to
• Highlights the dominant traits and the weakest strands in the DNA
• Gives leadership a baseline to track cultural and structural change over time

Module 42 — Culture Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Scores organizational culture signals (documentation discipline, ownership behavior, collaboration) into a single culture-health read.

What it does:

• Reads culture signals from real behavior — documentation discipline, ownership follow-through, collaboration patterns
• Scores each signal and combines them into a single culture-health index
• Surfaces the cultural strengths to protect and the habits that create risk
• Turns "culture" from a vague feeling into a measured, trackable number

Module 43 — Organizational Maturity Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Assesses overall organizational maturity across process, governance, and knowledge dimensions and places the org on a maturity curve.

What it does:

• Assesses maturity across process, governance, and knowledge dimensions
• Places the organization on a defined maturity curve (from ad-hoc to optimized)
• Identifies the specific gaps holding the org back from the next maturity stage
• Gives leadership a roadmap for structured improvement

Module 44 — Organizational Behavior Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Profiles how each actor behaves (ownership load, resolution activity, documentation habits) to surface behavioral risk and strengths.

What it does:

• Profiles how each actor behaves — ownership load, incident-resolution activity, documentation habits
• Flags behavioral risk (over-reliance on one person, documentation avoidance) and behavioral strengths
• Ranks actors by their real contribution and exposure
• Helps leadership reward the right behavior and coach the risky patterns

Module 45 — Benchmark Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Compares the organization's key metrics against industry baselines to show where it leads and where it lags.

What it does:

• Compares the organization's key metrics against industry baselines
• Shows exactly where the org leads and where it lags the benchmark
• Turns internal scores into external context leadership can act on
• Highlights the biggest gaps to close to reach industry-standard resilience

Module 46 — Truth Intelligence (gates Module 48)

Engineer: Kamran · GET /api/intelligence/truth

The constitutional truth layer: verifies every claimed fact against the underlying data, assigns a data-trust score, and only lets verified truths pass downstream. Enforces the core principle "truth before recommendation" — nothing reaches the Advisor until it is verified here.

What it does:

• Re-checks every downstream claim against the raw organizational data
• Assigns a confidence / data-trust score to each fact
• Blocks unverified or contradicted claims from moving forward
• Acts as the gate that Module 48 must pass through

Module 47 — Continuous Learning Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Evaluates how accurate past predictions turned out to be and validates whether recorded lessons are actually being applied — the system's self-check loop.

What it does:

• Checks how accurate past predictions turned out to be against what actually happened
• Validates whether recorded lessons are actually being applied, not just filed away
• Scores the organization's learning loop — is it getting smarter over time?
• Flags lessons that were ignored and predictions that missed, so the system self-corrects

Module 48 — Autonomous Advisor

Engineer: Kamran · GET /api/intelligence/advisor

Generates leadership recommendations only from truths verified by Module 46 — never from raw or unverified signals — so every recommendation is defensible and evidence-backed.

What it does:

• Reads only the verified truths passed through Module 46 (never raw or unverified signals)
• Generates specific, evidence-backed leadership recommendations
• Attaches the supporting truth and its confidence to every recommendation so it is defensible
• Refuses to recommend anything that Truth Intelligence has not verified

Module 49 — Digital Twin Intelligence

Engineer: Tahir · ships in horquva_modules_py/

Builds a live digital-twin snapshot of the organization, computes a twin health index, simulates scenarios against the twin, and checks that the twin stays synchronized with reality.

What it does:

• Builds a live digital-twin snapshot of the whole organization
• Computes a twin health index that mirrors the real organization's state
• Runs scenarios against the twin without touching production reality
• Continuously checks that the twin stays synchronized with the real organization

Module 50 — Organizational Brain Core Logic

Engineer: Kamran · GET /api/intelligence/brain-core

The reasoning core that fuses every verified signal into one brain index and an operating posture — the organization's current "state of mind" (stable, strained, or critical).

What it does:

• Fuses every verified signal from across the Brain into one unified brain index
• Determines the organization's current operating posture: STABLE / STRAINED / CRITICAL
• Explains the posture with the top contributing signals, not just a bare number
• Acts as the reasoning core the Orchestrator (M55) reads before its final verdict

Module 51 — Self-Healing Intelligence

Engineer: Anusha · GET /api/self-healing

Continuously scans for blocked workflows, actor collisions, single points of failure, policy breaks, and escalation conditions, then emits healing intents (pause / unblock / reassign) to Module 16 for governed execution. It never acts on its own — it detects and emits an intent, governed by the current execution mode (advisory by default).

What it does:

• Continuously scans for blocked workflows, actor collisions, single points of failure, policy breaks, and escalation conditions
• Emits healing intents (pause / unblock / reassign) to Module 16 for governed execution
• Never acts on its own — it only detects and proposes, governed by the active execution mode (advisory by default)
• Turns detected fragility into a safe, reviewable recovery action

Module 52 — Governance Automation Intelligence

Engineer: Anusha · GET/POST /api/automation/governance

Runs five governance rules over live activity, detects policy violations, and emits enforcement intents to Module 16 — turning Module 19's governance findings into governed action.

What it does:

• Runs five governance rules over live organizational activity
• Detects policy violations as they happen (missing owner, no backup, undocumented critical asset, stale policy, unaccountable action)
• Emits enforcement intents to Module 16 instead of acting directly
• Turns Module 19's governance findings into governed, auditable action

Module 53 — Continuity Automation Intelligence

Engineer: Anusha · GET/POST /api/automation/continuity

Detects five classes of continuity risk (owner loss, undocumented critical assets, single points of failure, and more) and emits recovery intents to Module 16 so the organization can respond before a disruption becomes an outage.

What it does:

• Detects five classes of continuity risk (owner loss, undocumented critical assets, single points of failure, missing backups, fragile workflows)
• Emits recovery intents to Module 16 before a risk becomes an outage
• Operates under the active governance mode so nothing auto-executes without authorization
• Turns Module 18's continuity findings into pre-emptive recovery action

Module 54 — Simulation Universe

Engineer: Kamran · GET /api/intelligence/simulation-universe

Runs a whole universe of what-if scenarios (people leaving, agents failing, tools going offline, cascading combinations) and ranks them by survivability so leadership sees exactly where the organization would break first.

What it does:

• Runs a whole universe of what-if scenarios — people leaving, agents failing, tools going offline, and cascading combinations
• Recalculates organizational survivability for every scenario
• Ranks all scenarios so leadership sees exactly where the organization breaks first
• Turns single what-if checks into a full stress-test of the entire organization

Module 55 — Organizational Intelligence Orchestrator (Meta-Brain)

Engineer: Kamran · GET /api/intelligence/orchestrator

The Meta-Brain. Runs last. It fuses every module's output into one Organizational Intelligence Score and a final verdict, enforcing the constitutional rule that the orchestrator only speaks after all verified intelligence is in.

What it does:

• Runs last — only after every other module's verified output is in
• Fuses all module outputs into one Organizational Intelligence Score and a final verdict
• Enforces the constitutional rule that the Meta-Brain speaks only on verified intelligence
• Delivers leadership the single top-level answer: how intelligent and resilient the organization really is

Constitutional layer (Kamran): Modules 36, 38, 39, 40, 46, 48, 50, 54, 55 form Phase 6 — served under /api/intelligence/* and printed at the end of uv run main.py. Truth (46) gates Advisor (48); Orchestrator (55) runs last.

Automation layer (Anusha): Modules 51, 52, 53 each detect → emit intent → Module 16 executes under the active governance mode. Automation follows intelligence — never automate an action that was not first verified. Modules 15, 16, 21, and 23 (also Anusha) are documented in sequence above.

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Constitutional Runtime — Organizational Brain (backend/brain/)

The 55 modules (M01–M55) are no longer standalone analyzers — they now boot and execute together as one constitutional Organizational Brain. This runtime is live Node.js code inside the API server and is split into the four ownership layers defined by the MVP Execution Guides.

Knowledge Platform — Huzaifa (backend/brain/knowledge/)

The discovery + memory foundation that turns raw organizational data into one shared, connected truth.

Component	File	Role
Module Registry Loader	knowledge/moduleRegistry.js	Auto-discovers & validates all 55 modules; rejects duplicates/invalid
Capability Registry	knowledge/capabilityRegistry.js	Turns modules into discoverable organizational services
Intelligence Exchange Protocol	knowledge/intelligenceExchange.js	Common language: validated Intelligence Packages + confidence propagation
Entity Registry	knowledge/entityRegistry.js	Every organizational object exists once (Single Source of Truth)
Relationship Registry	knowledge/relationshipRegistry.js	Relationships as first-class assets; no dangling edges
Unified Knowledge Graph	knowledge/knowledgeGraph.js	Brain's long-term memory: traversal, dependency paths, search
Ontology Runtime	data/ontology.js	One constitutional meaning per concept & relationship
Graph APIs	knowledge/graphApi.js	The only gateway to knowledge (registry + graph + exchange)

Brain Runtime — Kamran (backend/brain/runtime/)

The engineering brain that makes the 55 modules act as one organ.

Component	File	Role
Event & Signal Bus	runtime/eventBus.js	Event-driven backbone; loose coupling + observability
Brain State Manager	runtime/brainState.js	Lifecycle phase, module health, executions, boot report
Constitutional Communication Layer	runtime/communicationLayer.js	No module talks directly; every call is routed + contract-checked
Brain Execution Engine	runtime/executionEngine.js	Capability discovery + topological dependency ordering + fusion
Organizational Brain Runtime	runtime/runtime.js	Boots the whole Brain; produces the Boot Report
Constitutional API Gateway	runtime/brainApi.js	Executive APIs: /status, /boot-report, /ask, /plan, /signals

Prediction, Learning & Organizational Science — Tahir (backend/brain/modules/implementations.js)

The forward-looking and inward-looking intelligence. Every module below consumes the shared Knowledge Graph and returns a real Intelligence Package (prediction/insight + confidence + evidence + recommended action) — no stubs.

Module	Name	What it computes at runtime
M11	Predictive Risk	Projects each entity's future risk from dependency-cascade depth + ownership gaps; flags imminent, high-likelihood threats before they fail
M12	Forecasting	Activity-weighted 30/60/90-style outlook with best / expected / worst scenarios
M13	Human-AI Collaboration	AI-adoption vs. human-dependency balance and the collaboration orientation
M17	Organizational Learning	Learning maturity from intelligence produced across the graph + recent confidence trend
M32	Dependency Impact	Impact score & severity for every dependency; surfaces the highest-impact links
M33	Dependency Evolution	Criticality distribution, dependency cycles and directional trend
M37	Pattern	Structural anomalies — isolated nodes and over-connected hubs
M41	Organizational DNA	Human vs. automation share and the org's structural orientation
M42	Culture	Collaboration vs. silo signals, including siloed people and transitional signals
M43	Organizational Maturity	Maturity dimensions, current level and the gap to the next level
M44	Organizational Behavior	Dominant operating behavior and orientation
M45	Benchmark	Four internal benchmarks fused into a single benchmark score
M47	Continuous Learning	Confidence delta over time and the learning trend
M49	Digital Twin	A live twin snapshot of the organization across all intelligence layers

Executive Experience & Autonomous Operations — Anusha (backend/brain/modules/implementations.js)

The executive-facing surface and the autonomy layer. Automation always follows intelligence — nothing is auto-executed that was not first verified.

Module	Name	What it computes at runtime
M15	Verification	Per-asset verification rate from owners + intact dependencies; lists integrity errors
M16	Workflow Orchestration	Topological run order with owners, readiness and bottlenecks
M21	Executive Avatar	Role-aware executive persona and how each briefing opens
M23	Executive Briefing	Fuses health (M25), risk (M03), advisor (M48) and prediction (M11) into a role-aware briefing with prioritized recommendations
M51	Self-Healing	Detects issues, marks the auto-healable ones and emits the healing workflow
M52	Governance Automation	Compliance rate + the governance actions to auto-enforce
M53	Continuity Automation	Continuity score, resilience and a prioritized recovery plan

Real logic — no stubs

Every one of the 55 modules has a real implementation in backend/brain/modules/implementations.js that computes genuine intelligence from the knowledge graph (ownership coverage, single points of failure, dependency cascades, ownership concentration, governance gaps, health index, truth verification, autonomous advice, meta-fusion). Constitutional rules are enforced at runtime:

• Truth (M46) gates the Autonomous Advisor (M48) — advice is withheld unless truth is verified.
• Meta-Brain Orchestrator (M55) always runs last and fuses all module intelligence into one executive answer.
• Discovery before execution — no module is ever hard-referenced.

Run & test the Brain

# 1) Boot the Brain standalone and print the Boot Report (55/55 modules)
node backend/brain/boot.js

# 2) Run the full API server — the Brain auto-mounts at /api/brain
node backend/index.js

Key endpoints once the server is running:

GET  /api/brain/boot-report                     # acceptance report
GET  /api/brain/registry/modules?owner=Huzaifa  # discovered modules
GET  /api/brain/graph/entities                   # organizational reality
GET  /api/brain/graph/dependency-path/:id        # dependency chain
POST /api/brain/plan  { "modules": ["M03","M48","M55"] }   # constitutional order
POST /api/brain/ask   { "need": "risk", "context": { "role": "CEO" } }  # executive answer

Verify the whole system (boot & per-engineer health check)

Anyone can confirm — in under a minute, without a database — that all four engineers' modules boot and execute together as one system.

1) Boot the Brain and print the acceptance report:

node backend/brain/boot.js

The run is healthy when you see:

Accepted     : YES ✅
Modules      : 55/55 discovered
By owner     :  Huzaifa 13 · Kamran 21 · Tahir 14 · Anusha 7

plus every acceptance criterion checked and a demo executive query returning a fused confidence.

2) Confirm each engineer's modules return real output (with the API server running):

GET  /api/brain/registry/modules?owner=Huzaifa   # 13 modules
GET  /api/brain/registry/modules?owner=Kamran    # 21 modules
GET  /api/brain/registry/modules?owner=Tahir     # 14 modules
GET  /api/brain/registry/modules?owner=Anusha    # 7 modules
POST /api/brain/ask  { "need": "biggest organizational risks", "context": { "role": "CEO" } }

A healthy ask response returns a numeric fusedConfidence and a results array in which every module carries a filled payload (never empty) — proof there are no stubs anywhere in the pipeline.

How to read the result — the system is healthy when:

• The Boot Report says Accepted: YES with 55/55 and owner counts 13 / 21 / 14 / 7.
• GET /api/brain/graph/validate reports the graph is valid.
• POST /api/brain/ask returns a fused confidence and populated per-module payloads.

Tip: node backend/brain/boot.js runs standalone — no Supabase and no .env required — so it is the fastest single proof that Huzaifa's, Kamran's, Tahir's and Anusha's work all functions together. The API server is only needed for the HTTP endpoints.

Full details: see backend/brain/README.md.

---

Demo Results Summary

Metric	Result
Total Agents Analyzed	15
CRITICAL Risk Agents	5
HIGH Risk Agents	6
Single Points of Failure (Agent)	4
Human Single Points of Failure	1 (Robert)
Robert's Agents (zero backups)	5 → all CRITICAL
Worst Scenario: Robert Leaves	Health Score: 56 → 49
Organizational Health Score	56/100 — AT RISK
Institutional Memory Health Score	54/100 — AT RISK
Actionable Recommendations Generated	12
Total Coverage Gaps	9
Total Undocumented Assets	13
Total Knowledge Gaps	15
Total Monthly AI Tool Spend	$1,444
Total Actions Verified (Module 15)	36
Total Workflow Collisions Detected (Module 16)	17
Total Decisions Audited (Module 14)	27
Decision Quality Index (Module 14)	67/100 — MIXED
Assets That Must Be Protected (Module 18)	10
Organizational Continuity Score (Module 18)	63/100 — AT RISK
Predicted Critical Threats (Module 11)	4
90-Day Organizational Outlook (Module 12)	52/100 — AT RISK
AI Adoption / Human Dependency / Collaboration (Module 13)	100 / 54 / 40
Learning Maturity (Module 17)	40/100 — EARLY STAGE
Governance Score (Module 19)	47/100 — AT RISK
Accountability Score (Module 20)	76/100 — WARNING
Organizational Intelligence Score (Engine — Five Pillars)	57/100 — WEAK
Ontology Entities Registered (Layer A1)	55 across 6 types
Relationships Mapped (Layer A2)	138
Reasoning Insights Generated (Layer A3)	16 (9 CRITICAL)
Contradictions Resolved by Truth Layer (Layer A4)	18
Brain Trust Score (Layer A4)	75%
Context Packages Built (Layer A5)	9 scopes
Voice-Resolvable Entities (Layer A6)	35 (76 aliases)
Decision Queue (Module 24)	19 prioritized
Organizational Health (Module 25)	46/100 — WARNING (improving)
Universal Dependency Graph (Module 28)	39 nodes · 58 edges
Knowledge Bus Factor (Module 30)	2
Hidden Dependencies Found (Module 34)	18

---

How to Run

1 — Python Intelligence Engine

Runs all constitutional modules (M01–M55) in sequence and prints full analysis to the terminal. Phase 6 (M36–M55) prints at the end.

# Install dependencies (requires uv)
uv sync

# Run all constitutional modules M01–M55
uv run main.py

This project uses uv as the Python package manager. All dependencies are declared in pyproject.toml and locked in uv.lock.

---

2 — Backend API (Node.js + Express + Supabase)

cd backend

# Install dependencies
npm install

# Start the server
node index.js

Server starts on http://localhost:3000

⚠️ Run backend commands from inside the backend/ folder (cd backend). The repo root has no package.json, and .env must live in backend/. The server loads backend/.env by absolute path, so node backend/index.js from the repo root also works once dependencies are installed.

The Organizational Brain auto-mounts at /api/brain as the server boots — watch for the log line Organizational Brain: READY — 55/55 modules. To boot and verify the Brain on its own (no server, no Supabase needed):

node backend/brain/boot.js   # prints the Boot Report (55/55 modules) + a demo executive query

Key Brain endpoints once the server is running:

Endpoint	Method	Description
/api/brain/boot-report	GET	Acceptance report — 55/55 modules and all criteria
/api/brain/status	GET	Live runtime phase + module health
/api/brain/graph/entities	GET	Organizational entities in the Knowledge Graph
/api/brain/registry/modules?owner=Huzaifa	GET	Discovered modules (filter by owner)
/api/brain/plan	POST	Constitutional execution order for { "modules": ["M03","M48","M55"] }
/api/brain/ask	POST	Executive answer for { "need": "risk", "context": { "role": "CEO" } }

All API Endpoints

Endpoint	Module	Description
GET /api/agents	01	All agents with ownership, risk level, and metadata
GET /api/ownership	01	Owners mapped to their agents with risk scores
GET /api/dependencies	02	Full dependency graph with cascade relationships
GET /api/risks	03	Composite risk score breakdown per agent
GET /api/dashboard	03	Executive summary: health score, critical counts, orphan count
GET /api/human-agent-map	06	Person → agents ownership tree with coverage scores
GET /api/tools	07	All AI tools with user counts and risk levels
GET /api/tool-intelligence	07	Tool risk analysis with department exposure
GET /api/tool-impact	07	Impact simulation: what breaks if a tool goes offline
GET /api/workflows	08	All workflows with step chains and risk scores
GET /api/knowledge/intelligence	09	Knowledge concentration scores per person
GET /api/knowledge/impact	09	Asset loss mapping per person departure
GET /api/knowledge/gaps	09	All undocumented assets with no backup
GET /api/memory	10	Institutional memory status per asset
GET /api/simulations/employee-leaves	05	Health Score impact when a person leaves
GET /api/simulations/agent-fails	05	Health Score impact when an agent fails
GET /api/simulations/platform-down	05	Health Score impact when a tool goes offline
GET /api/simulations/workflow-disruption	05	Health Score impact when a workflow breaks
GET /api/predictive-risk/summary	11	Predicted risk counts: critical, high, medium, emerging
GET /api/predictive-risk/agents	11	Per-agent predicted risk escalation with classification
GET /api/predictive-risk/critical	11	Only agents predicted to reach CRITICAL risk
GET /api/predictive-risk/emerging	11	Emerging threats detected before failure
GET /api/predictive-risk/agent/:name	11	Predicted risk detail for a single agent
GET /api/forecast/summary	12	30/60/90-day organizational outlook summary
GET /api/forecast/health	12	Forecasted organizational health trajectory
GET /api/forecast/memory	12	Forecasted institutional memory trajectory
GET /api/forecast/continuity	12	Forecasted continuity / survival trajectory
GET /api/forecast/outlook	12	Overall organizational outlook score
GET /api/collaboration/adoption	13	AI adoption across the workforce
GET /api/collaboration/dependency	13	Human dependency concentration
GET /api/collaboration/score	13	Human-AI collaboration effectiveness score
GET /api/collaboration/people	13	Per-person collaboration profile
GET /api/collaboration/departments	13	Per-department collaboration breakdown
GET /api/decisions/index	14	Decision Quality Index
GET /api/decisions/all	14	All reconstructed decisions with quality scores
GET /api/decisions/harmful	14	Decisions scored as HARMFUL
GET /api/decisions/trail/:id	14	Full decision trail for one decision
GET /api/decisions/recommendations	14	Decision-improvement recommendations
GET /api/verification/summary	15	Total counts: completed, flagged, violations
GET /api/verification/actions	15	All tracked actions (human / agent / tool)
GET /api/verification/flagged	15	Only flagged / non-compliant actions
GET /api/verification/actor/:name	15	Verification record for a single actor
GET /api/orchestration/summary	16	Total counts: running, blocked, collisions
GET /api/orchestration/workflows	16	All workflow orchestration states
GET /api/orchestration/collisions	16	Detected actor collisions across workflows
GET /api/orchestration/blocked	16	Workflows currently blocked by shared resources
GET /api/learning/summary	17	Learning Maturity Score summary
GET /api/learning/failures	17	Failure patterns analyzed across the org
GET /api/learning/decisions	17	Learning signals derived from past decisions
GET /api/learning/incidents	17	Incident exposure history
GET /api/learning/departments	17	Per-department learning maturity
GET /api/continuity/score	18	Organizational continuity score
GET /api/continuity/assets	18	Asset survival: SURVIVES / DEGRADED / FAILS / LOST
GET /api/continuity/risk-map	18	Continuity risk map across assets
GET /api/continuity/must-protect	18	Critical assets that must be protected
GET /api/continuity/plans	18	Generated continuity plans for critical assets
GET /api/governance/score	19	Governance score
GET /api/governance/assets	19	Per-asset governance status
GET /api/governance/heatmap	19	Governance heatmap by department
GET /api/governance/gaps	19	Ownership and documentation gaps
GET /api/governance/offenders	19	Worst governance offenders
GET /api/accountability/score	20	Accountability score (RACI model)
GET /api/accountability/entities	20	RACI entities: Responsible / Accountable / Consulted / Informed
GET /api/accountability/chains	20	Accountability chains across the org
GET /api/accountability/issues	20	Accountability gaps and conflicts
GET /api/avatar/escalations	21	Executive-avatar escalation log
GET /api/avatar/escalations/critical	21	Critical escalations only
GET /api/avatar/escalations/summary	21	Escalation counts by severity + status
POST /api/avatar/check	21	Gate-check a workflow; auto-escalate on failure
POST /api/voice/transcribe	22	Transcribe text/audio into a transcript
POST /api/voice/intent	22	Parse a transcript into a structured intent
GET /api/briefing/latest	23	Full executive briefing (health + risks + recs)
GET /api/briefing/risks	23	Briefing risk summary
GET /api/briefing/health	23	Briefing org-health snapshot
GET /api/briefing/recommendations	23	Briefing action recommendations
GET /api/self-healing/...	51	Detect issues + emit healing intents to M16
GET /api/automation/governance/audit	52	Detect governance violations
POST /api/automation/governance/enforce	52	Emit governance-enforcement intents to M16
GET /api/automation/continuity/risks	53	Detect continuity risks
POST /api/automation/continuity/plan	53	Emit continuity-recovery intents to M16
GET /api/intelligence	Phase 6	Index of all Phase 6 endpoints
GET /api/intelligence/signals	36	Early-warning stability score + active signals
GET /api/intelligence/opportunities	38	Prioritised opportunity backlog + quick wins
GET /api/intelligence/capability	39	Capability index per department
GET /api/intelligence/alignment	40	Alignment index + misaligned areas
GET /api/intelligence/truth	46	Verified truths + data trust score
GET /api/intelligence/advisor	48	Recommendations from verified truths only
GET /api/intelligence/brain-core	50	Brain index + operating posture
GET /api/intelligence/simulation-universe	54	Ranked what-if scenarios + survivability
GET /api/intelligence/orchestrator	55	Organizational Intelligence Score + verdict

Environment Setup

# 1. Copy the template
cp backend/.env.example backend/.env

# 2. Create the database tables (run once) — paste backend/schema.sql
#    into the Supabase SQL editor and run it

Fill in your Supabase credentials in backend/.env:

SUPABASE_URL=your_supabase_project_url
SUPABASE_KEY=your_supabase_secret_key
PORT=3000

.env is git-ignored and must never be committed to version control.

---

3 — Executive Frontend Dashboard

cd frontend

# Install dependencies
npm install

# Start the dev server
npm run dev

Dashboard runs on http://localhost:3001

Also accessible on your local network at http://<your-ip>:3001

---

Project Structure

OBA-Core-Horquva/
│
├── data/
│   └── sunrise_care.json                      # Demo dataset (120 employees, 15 agents)
│
├── modules/
│   ├── __init__.py
│   ├── ownership_intelligence.py              # Module 01 — Ownership Intelligence
│   ├── dependency_intelligence.py             # Module 02 — Dependency Intelligence
│   ├── risk_intelligence.py                   # Module 03 — Risk Intelligence
│   ├── recommendation_engine.py               # Module 04 — Recommendation Engine
│   ├── whatif_simulation.py                   # Module 05 — What-If Simulation
│   ├── human_agent_map.py                     # Module 06 — Human-Agent Map
│   ├── ai_tool_intelligence.py                # Module 07 — AI Tool Intelligence
│   ├── workflow_intelligence.py               # Module 08 — Workflow Intelligence
│   ├── knowledge_risk_intelligence.py         # Module 09 — Knowledge Risk Intelligence
│   ├── organizational_memory_intelligence.py  # Module 10 — Organizational Memory
│   ├── predictive_risk_intelligence.py        # Module 11 — Predictive Risk Intelligence
│   ├── organizational_forecasting_intelligence.py # Module 12 — Organizational Forecasting
│   ├── human_ai_collaboration_intelligence.py # Module 13 — Human-AI Collaboration
│   ├── decision_intelligence.py               # Module 14 — Decision Intelligence
│   ├── verification_intelligence.py           # Module 15 — Verification Intelligence
│   ├── workflow_orchestration_intelligence.py # Module 16 — Workflow Orchestration
│   ├── organizational_learning_intelligence.py # Module 17 — Organizational Learning
│   ├── organizational_continuity_intelligence.py # Module 18 — Organizational Continuity
│   ├── governance_intelligence.py             # Module 19 — Governance Intelligence
│   ├── accountability_intelligence.py         # Module 20 — Accountability Intelligence
│   ├── data_models.py                         # Phase 2 — Platform Foundation: data models
│   ├── intelligence_pipeline.py               # Phase 2 — Platform Foundation: pipeline
│   ├── governance_data_framework.py           # Phase 2 — Platform Foundation: governance framework
│   ├── storage_layer.py                       # Phase 2 — Platform Foundation: storage layer
│   ├── organizational_intelligence_engine.py  # Phase 2 — Organizational Intelligence Engine
│   │
│   │   # Phase 6 — Constitutional Intelligence & Meta-Brain (M36–M55, Kamran)
│   ├── signal_intelligence.py                 # Module 36 — Signal Intelligence
│   ├── opportunity_intelligence.py            # Module 38 — Opportunity Intelligence
│   ├── capability_intelligence.py             # Module 39 — Capability Intelligence
│   ├── strategic_alignment_intelligence.py    # Module 40 — Strategic Alignment Intelligence
│   ├── truth_intelligence.py                  # Module 46 — Truth Intelligence (gates M48)
│   ├── autonomous_advisor.py                  # Module 48 — Autonomous Advisor (only verified truths)
│   ├── brain_core_logic.py                    # Module 50 — Organizational Brain Core Logic
│   ├── simulation_universe.py                 # Module 54 — Simulation Universe
│   └── intelligence_orchestrator.py           # Module 55 — Intelligence Orchestrator (Meta-Brain, runs last)
│
├── horquva_modules_py/                        # Prediction · Learning · Org-Science package (Tahir, M32–M49)
│   ├── __init__.py
���   ├── m32_dependency_impact_intelligence.py       # Module 32 — Dependency Impact Intelligence
│   ├── m33_dependency_evolution_intelligence.py    # Module 33 — Dependency Evolution Intelligence
│   ├── m37_pattern_intelligence.py                 # Module 37 — Pattern Intelligence
│   ├── m41_organizational_dna.py                   # Module 41 — Organizational DNA Intelligence
│   ├── m42_culture_intelligence.py                 # Module 42 — Culture Intelligence
│   ├── m43_organizational_maturity_intelligence.py # Module 43 — Organizational Maturity Intelligence
│   ├── m44_organizational_behavior_intelligence.py # Module 44 — Organizational Behavior Intelligence
│   ├── m45_benchmark_intelligence.py               # Module 45 — Benchmark Intelligence
│   ├── m47_continuous_learning_intelligence.py     # Module 47 — Continuous Learning Intelligence
│   ├── m49_digital_twin_intelligence.py            # Module 49 — Digital Twin Intelligence
│   ├── demo.py                                 # Runs all 10 modules on the dataset
│   └── README.md                               # Package usage guide
│
├── backend/
│   ├── index.js                               # Express server — all routes registered here
│   ├── supabase.js                            # Supabase client — loads backend/.env by absolute path (works from any working directory)
│   ├── schema.sql                             # Supabase tables — run once before starting the server
│   ├── API_REFERENCE.md                       # Full endpoint reference for the frontend team
│   ├── package.json                           # Node.js dependencies
│   ├── .env.example                           # Environment variable template
│   ├── brain/                                 # Organizational Brain Runtime — all 55 modules boot as ONE constitutional system
│   │   ├── boot.js                            # CLI: boots the Brain and prints the Boot Report → `node backend/brain/boot.js`
│   │   ├── index.js                           # bootBrain() / getBrain() / mountBrain(app) — auto-mounts at /api/brain
│   │   ├── README.md                          # Brain runtime documentation
│   │   ├── data/
│   │   │   ├── constitutional-modules.js      # LOCKED M01–M55 catalog (code, name, owner, layer, capability)
│   │   │   └── ontology.js                    # Entity + relationship types (shared organizational meaning)
│   │   ├── knowledge/                         # Huzaifa — Knowledge Platform (discovery + memory)
│   │   │   ├── moduleRegistry.js              # Auto-discovers & validates all 55 modules
│   │   │   ├── capabilityRegistry.js          # Discoverable constitutional capabilities
│   │   │   ├── intelligenceExchange.js        # Intelligence Package format + confidence propagation
│   │   │   ├── entityRegistry.js              # Single source of truth for entities
│   │   │   ├── relationshipRegistry.js        # Relationships as first-class assets
│   │   │   ├── knowledgeGraph.js              # Unified Organizational Knowledge Graph (long-term memory)
│   │   │   ├── graphApi.js                    # Graph + registry REST router
│   │   │   └── graphSeeder.js                 # Seeds a demo organization (16 entities, 24 relationships)
│   │   ├── runtime/                           # Kamran — Brain Runtime (makes the 55 modules act as one organ)
│   │   │   ├── eventBus.js                    # Event & Signal Bus (+ journal)
│   │   │   ├── brainState.js                  # Lifecycle phase, module health, executions, boot report
│   │   │   ├── communicationLayer.js          # Constitutional routing (no stubs — throws if a capability is unbound)
│   │   │   ├── executionEngine.js             # Dependency ordering (topological) + constitutional rules + fusion
│   │   │   ├── runtime.js                     # Boots the whole Brain; produces the Boot Report
│   │   │   └── brainApi.js                    # Constitutional API router (/status, /boot-report, /ask, /plan, /signals)
│   │   └── modules/                           # REAL per-module logic (no stubs) for all 55 modules
│   │       ├── analytics.js                   # Shared graph analytics (ownership, SPOF, cascades, centrality, cycles)
│   │       ├── implementations.js             # M01–M55 real implementations (graph-derived intelligence)
│   │       └── index.js                       # bindAll(runtime): binds every capability to its real implementation
│   └── routes/
│       ├── agents.js                          # /api/agents
│       ├── ownership.js                       # /api/ownership
│       ├── dependencies.js                    # /api/dependencies
│       ├── risks.js                           # /api/risks
│       ├── dashboard.js                       # /api/dashboard
│       ├── humanAgentMap.js                   # /api/human-agent-map
│       ├── tools.js                           # /api/tools
│       ├── toolIntelligence.js                # /api/tool-intelligence
│       ├── toolImpact.js                      # /api/tool-impact
│       ├── simulations/
│       │   ├── employeeLeaves.js              # /api/simulations/employee-leaves
│       │   ├── agentFails.js                  # /api/simulations/agent-fails
│       │   ├── platformDown.js                # /api/simulations/platform-down
│       │   └── workflowDisruption.js          # /api/simulations/workflow-disruption
│       ├── workflows/
│       │   ├── index.js                       # /api/workflows
│       │   ├── intelligence.js
│       │   ├── failures.js
│       │   └── spof.js
│       ├── knowledge/
│       │   ├── intelligence.js                # /api/knowledge/intelligence
│       │   ├── impact.js                      # /api/knowledge/impact
│       │   └── gaps.js                        # /api/knowledge/gaps
│       ├── memory/
│       │   └── memory.js                      # /api/memory
│       ├── predictive/
│       │   └── predictiveRisk.js              # /api/predictive-risk (Module 11)
│       ├── forecast/
│       │   └── forecast.js                    # /api/forecast (Module 12)
│       ├── collaboration/
│       │   └── collaboration.js               # /api/collaboration (Module 13)
│       ├── decisions/
│       │   └── decisions.js                   # /api/decisions (Module 14)
│       ├── verification/
│       │   └── index.js                       # /api/verification (Module 15 — Anusha)
│       ├── orchestration/
│       │   ├── index.js                       # /api/orchestration (Module 16 — Anusha)
│       │   ├── intentReceiver.js              # Receives intents from M51/M52/M53
│       │   └─�� executionEngine.js             # Executes governed intents by mode
│       ├── learning/
│       │   └── learning.js                    # /api/learning (Module 17)
│       ├── continuity/
│       │   ├── continuity.js                  # /api/continuity (Module 18 — Kamran, read)
│       │   ├── index.js                       # /api/automation/continuity (Module 53 — Anusha)
│       │   └── continuityEngine.js            # Continuity risk detection + recovery intents
│       ├── governance/
│       │   ├── governance.js                  # /api/governance (Module 19 — Kamran, read)
│       │   ├── index.js                       # /api/automation/governance (Module 52 — Anusha)
│       │   └── governanceEngine.js            # Policy-violation detection + enforcement intents
│       ��── accountability/
│       │   └── accountability.js              # /api/accountability (Module 20)
│       ├── avatar/
│       │   ├── index.js                       # /api/avatar (Module 21 — Anusha)
│       │   ├── gateCheck.js                   # Workflow gate-check logic
│       │   └── escalate.js                    # Escalation logging
│       ├── briefing/
│       │   ├── index.js                       # /api/briefing (Module 23 — Anusha)
│       │   ├── briefingEngine.js              # Builds the executive briefing
│       │   └── recommendations.js             # Briefing recommendations
│       ├── selfHealing/
│       │   ├── index.js                       # /api/self-healing (Module 51 — Anusha)
│       │   └── healingEngine.js               # Issue detection + healing intents to M16
│       ├── voice/
│       │   ├── index.js                       # /api/voice (Module 22 — Huzaifa)
│       │   ├── stt.js                         # Speech-to-text transcription
│       │   └── intentParser.js                # Transcript → structured intent
│       └── intelligence/
│           └── constitutional.js              # /api/intelligence/* — Phase 6 endpoints (M36–M55)
│
├── frontend/
│   ├── app/
│   │   ├── layout.tsx                         # Shell: persistent sidebar navigation
│   │   ├── globals.css                        # Design system, tokens, dark theme
│   │   ├── page.tsx                           # Screen 1: Executive Dashboard
│   │   ├── ownership/page.tsx                 # Screen 2: Ownership Intelligence
│   │   ├── risk/page.tsx                      # Screen 3: Risk Intelligence
│   │   ├── map/page.tsx                       # Screen 4: Dependency Map
│   │   ├── simulation/page.tsx                # Screen 5: What-If Simulation
│   │   └── recommendations/page.tsx           # Screen 6: Recommendations
│   ├── components/
│   │   ├── layout/
│   │   │   ├── Sidebar.tsx                    # Navigation sidebar (6 routes)
│   │   │   └── Topbar.tsx                     # Top navigation bar
│   │   ├── dashboard/
│   │   │   ├── KpiStrip.tsx                   # Key metrics strip
│   │   │   ├── Heatmap.tsx                    # Agent risk heatmap
│   │   │   ├── RiskSplit.tsx                  # Risk tier distribution chart
│   │   │   └── AgentTable.tsx                 # Full agent data table
│   │   ├── ownership/
│   │   │   ├── OwnershipOverview.tsx          # Ownership summary panel
│   │   │   ├── ConcentrationBar.tsx           # Owner concentration bar chart
│   │   │   ├── OwnershipList.tsx              # Per-owner agent list
│   │   │   ├── HumanDependencyRisks.tsx       # Human SPOF indicators
│   │   │   ├── DependencyPipeline.tsx         # Dependency pipeline view
│   │   │   └─�� OrgRelationshipMap.tsx         # Org-level relationship map
│   │   ├── risk/
│   │   │   ├── RiskHeader.tsx                 # Risk page header with health score
│   │   │   ├── OrgHealthBanner.tsx            # Org Health Score banner
│   │   │   ├── CriticalRiskPanel.tsx          # CRITICAL agents panel
│   │   │   └── RiskScoreTable.tsx             # Full risk score table
│   │   ├── map/
│   │   │   ├── FlowCanvas.tsx                 # Interactive dependency flow diagram
│   │   │   ├── CustomNodes.tsx                # Custom node renderers
│   │   │   ├── DependencyKPIs.tsx             # Dependency KPI cards
│   │   │   └── DependencyTable.tsx            # Dependency data table
│   │   ├── simulation/
│   │   │   ├── SimulationDashboard.tsx        # Simulation control panel
│   │   │   ├── ScenarioRanking.tsx            # Scenarios ranked by impact
│   │   │   └── ImpactSummary.tsx              # Before/after impact summary
│   │   └── recommendations/
│   │       ├── RecommendationHeader.tsx       # Recommendations page header
│   │       ├── Top5Urgent.tsx                 # Top 5 urgent actions
│   │       ├── RecommendationList.tsx         # Full recommendations list
│   │       └── DemoSummary.tsx                # Final demo summary panel
│   ├── lib/
│   │   ├── data.ts                            # Server-side JSON data loader
│   │   ├── graph.ts                           # Graph traversal and cascade logic
│   │   ├── risk.ts                            # Risk scoring utilities
│   │   ├── simulation.ts                      # What-If scenario engine (TS)
│   │   └── recommendations.ts                 # Recommendation generation logic
│   └── types/
│       └── index.ts                           # TypeScript type definitions
│
├── Images/                                    # All module output screenshots
├── main.py                                    # Runs all constitutional modules (M01–M55) in sequence
├── HOWTO_RUN_AND_CHECK.md                     # How to run the engine, start the backend, and verify every route
├── INTEGRATION_STATUS.md                      # Team ownership, integration decisions, and verification results
├── pyproject.toml                             # Python project dependencies
└── uv.lock                                    # Locked Python dependency versions

---

Full Tech Stack

Layer	Component	Technology
Intelligence Engine	Core Logic	Python 3.13
Intelligence Engine	Package Manager	uv
Intelligence Engine	Terminal Output	rich
Intelligence Engine	Data Format	JSON
Backend	Server Framework	Node.js + Express 5
Backend	Database	Supabase (PostgreSQL)
Backend	DB Client	@supabase/supabase-js
Backend	Environment	dotenv
Frontend	Framework	Next.js 16 (Turbopack)
Frontend	Language	TypeScript
Frontend	Styling	Tailwind CSS v4
Frontend	Charts	Recharts
Frontend	Icons	Lucide React
Both	Version Control	GitHub

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Module Engineering

Module	Name	Lead Engineer
Module 01	Ownership Intelligence	Huzaifa
Module 02	Dependency Intelligence	Huzaifa
Module 03	Risk Intelligence	Huzaifa
Module 04	Recommendation Engine	Kamran
Module 05	What-If Simulation Engine	Kamran
Module 06	Human-Agent Dependency Map	Kamran
Module 07	AI Tool Intelligence	Huzaifa
Module 08	Workflow Intelligence	Huzaifa
Module 09	Knowledge Risk Intelligence	Kamran
Module 10	Organizational Memory Intelligence	Kamran
Module 11	Predictive Risk Intelligence	Tahir
Module 12	Organizational Forecasting Intelligence	Tahir
Module 13	Human-AI Collaboration Intelligence	Tahir
Module 14	Decision Intelligence	Kamran
Module 15	Verification Intelligence	Anusha
Module 16	Workflow Orchestration Intelligence	Anusha
Module 17	Organizational Learning Intelligence	Tahir
Module 18	Organizational Continuity Intelligence	Kamran
Module 19	Governance Intelligence	Huzaifa
Module 20	Accountability Intelligence	Huzaifa
Phase 2	Intelligence Platform Foundation	Huzaifa
Phase 2	Organizational Intelligence Engine (Five Pillars Integration)	Kamran
Layer A1	Ontology Layer (Defines What Exists)	Huzaifa
Layer A2	Relationship Layer (Defines How Everything Connects)	Huzaifa
Layer A3	Reasoning Layer (Turns Signals Into Understanding)	Kamran
Layer A4	Truth Layer (One Organizational Truth)	Kamran
Layer A5	Context Intelligence Layer (Real-Time Executive Context)	Huzaifa
Layer A6	Voice Agent Context Layer (Semantic Foundation for Voice)	Huzaifa
Module 21	Executive Avatar Intelligence	Anusha
Module 22	Voice Intelligence Engine	Huzaifa
Module 23	Executive Briefing Intelligence	Anusha
Module 24	Decision Support Intelligence	Kamran
Module 25	Organizational Health Intelligence	Kamran
Module 26	Executive Memory Intelligence	Kamran
Module 27	Executive Context Intelligence	Kamran
Module 28	Universal Dependency Graph	Huzaifa
Module 29	Organizational Relationship Intelligence	Huzaifa
Module 30	Knowledge Concentration Intelligence	Kamran
Module 31	Organizational Ecosystem Intelligence	Huzaifa
Module 32	Dependency Impact Intelligence	Tahir
Module 33	Dependency Evolution Intelligence	Tahir
Module 34	Hidden Dependency Intelligence	Huzaifa
Module 35	Organizational Network Intelligence	Huzaifa
Module 36	Signal Intelligence	Kamran
Module 37	Pattern Intelligence	Tahir
Module 38	Opportunity Intelligence	Kamran
Module 39	Capability Intelligence	Kamran
Module 40	Strategic Alignment Intelligence	Kamran
Module 41	Organizational DNA Intelligence	Tahir
Module 42	Culture Intelligence	Tahir
Module 43	Organizational Maturity Intelligence	Tahir
Module 44	Organizational Behavior Intelligence	Tahir
Module 45	Benchmark Intelligence	Tahir
Module 46	Truth Intelligence (gates M48)	Kamran
Module 47	Continuous Learning Intelligence	Tahir
Module 48	Autonomous Advisor	Kamran
Module 49	Digital Twin Intelligence	Tahir
Module 50	Organizational Brain Core Logic	Kamran
Module 51	Self-Healing Intelligence	Anusha
Module 52	Governance Automation Intelligence	Anusha
Module 53	Continuity Automation Intelligence	Anusha
Module 54	Simulation Universe	Kamran
Module 55	Organizational Intelligence Orchestrator (Meta-Brain)	Kamran

Runtime implementation (backend/brain/). All 55 modules boot and execute together as one constitutional runtime, documented in the Constitutional Runtime — Organizational Brain section. Every module has a real, graph-derived implementation in backend/brain/modules/implementations.js — there are no stub responses. Runtime ownership spans all four engineers: Huzaifa — Knowledge Platform (backend/brain/knowledge/); Kamran — Brain Runtime, core reasoning & Meta-Brain (backend/brain/runtime/); Tahir — Prediction, Learning & Organizational Science (M11, M12, M13, M17, M32, M33, M37, M41–M45, M47, M49); Anusha — Executive Experience & Autonomous Operations (M15, M16, M21, M23, M51, M52, M53).

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Phase 6 — Constitutional Intelligence & Meta-Brain (Master Registry M01–M55, LOCKED)

The Master Module Registry (M01–M55) is the single source of truth for OBA Core. Module definitions are locked — no renaming, merging, or duplication.

Phase 6 completes Kamran's constitutional modules. These build on the truth-before-recommendation principle: M46 (Truth) verifies before M48 (Advisor) recommends, and M55 (Orchestrator) fuses everything and is run last.

New modules (Kamran)

Module	Name	Layer	Owner
Module 36	Signal Intelligence	Intelligence	Kamran
Module 38	Opportunity Intelligence	Intelligence	Kamran
Module 39	Capability Intelligence	Intelligence	Kamran
Module 40	Strategic Alignment Intelligence	Intelligence	Kamran
Module 46	Truth Intelligence (gates M48)	Truth	Kamran
Module 48	Autonomous Advisor	Simulation	Kamran
Module 50	Organizational Brain Core Logic	Truth	Kamran
Module 54	Simulation Universe	Simulation	Kamran
Module 55	Organizational Intelligence Orchestrator (Meta-Brain)	Meta-Brain	Kamran

Locked assignment summary (M01–M55 = 55 modules)

Engineer	Modules	Count
Muhammad Huzaifa	M01, M02, M03, M07, M08, M19, M20, M22, M28, M29, M31, M34, M35	13
Kamran	M04, M05, M06, M09, M10, M14, M18, M24, M25, M26, M27, M30, M36, M38, M39, M40, M46, M48, M50, M54, M55	21
Muhammad Tahir	M11, M12, M13, M17, M32, M33, M37, M41, M42, M43, M44, M45, M47, M49	14
Anusha	M15, M16, M21, M23, M51, M52, M53	7

Run the Phase 6 modules (CLI)

# from repo root
uv run main.py        # runs all modules M01–M55, Phase 6 prints at the end

Phase 6 backend endpoints

Every Phase 6 endpoint (M36, M38, M39, M40, M46, M48, M50, M54, M55, plus the GET /api/intelligence index) is listed with full descriptions in the All API Endpoints table above, and verification steps are in backend/readme.md and HOWTO_RUN_AND_CHECK.md.

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Contribution & Review Process

All development on the OBA Core platform follows a centralized review workflow. Every team member's work — across the AI, Backend, and Frontend teams — is first submitted to Kamran Ai Engineer(Technical Lead) for review. Each member's files and modules are reviewed, validated, and integrated by Kamran to ensure constitutional consistency, code quality, and architectural alignment across all 55 modules (M01–M55). Only after this review are the changes pushed to the GitHub main branch. This process guarantees that every contribution meets the project's engineering standards and preserves a single, unified source of truth.

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Release

This repository represents the MVP release of Horquva Organizational Brain Analysis (OBA) Core, delivering the complete M01–M55 constitutional module engine, integrated backend APIs, and the executive frontend dashboard.