boc/docs/design/LANDVEX_PLATFORM_ARCHITECTURE.md

LandveX Platform Architecture

Five Engines

LandveX is not an image analysis platform. It is an economic control system for control intelligence.

Five engines work together:

Reality Engine
  ↓
Knowledge Engine
  ↓
Decision Engine
  ↓
Mission Engine
  ↓
Economic Engine

User experiences sit on top:

• quiXzoom — data collection (Zoomers)
• LandveX Dashboard — decision makers
• Intelligence Lab — development and validation

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1. Reality Engine

Purpose: Capture reality from the field.

Flow:

Phone → Video/Images → Upload → Immutable Archive

Key objects:

• ArchiveArtifact — original file, never changed
• FieldSession — organizes field work
• Mission — single data collection task

Value question: What reality was captured?

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2. Knowledge Engine

Purpose: Convert raw data to structured knowledge.

Flow:

Archive Artifact → Knowledge Extraction → Knowledge Graph

Key objects:

• KnowledgeArtifact — extracted knowledge (observations, segmentations, embeddings)
• Observation — what was seen
• Evidence — supporting data
• Finding — interpreted result

Value question: What does it mean in our domain?

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3. Decision Engine

Purpose: Produce verified decisions from knowledge.

Flow:

Finding → Decision → Review → Approved Decision Case

Key objects:

• DecisionCase — complete decision chain
• Review — human validation
• Decision — recommended action

Value question: What should we do?

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4. Mission Engine

Purpose: Generate and manage data collection missions.

Flow:

Knowledge Gap → Coverage Analysis → Mission Proposal → Budget Check → Mission Created

Key objects:

• KnowledgeGap — missing information
• Hotspot — high-value area
• Contradiction — conflicting information
• Mission — data collection task

Value question: Where should we collect data?

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5. Economic Engine

Purpose: Manage budgets, credits, and ROI.

Flow:

Budget → Credit Allocation → Mission Funding → Verified Delivery → Settlement → ROI

Key objects:

• Credit — first-class object (Mission, Validation, Training, Priority, Emergency)
• IntelligenceLedger — tracks value creation
• Settlement — payment to Zoomers

Value question: What did this decision cost?

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Cross-Cutting Objects

Contradiction Engine

Source A vs Source B → Confidence → Potential Value → Suggested Mission

Example:

• Municipality register: "Road is newly paved"
• Our observations: "Severe cracking"
• System: "Verify this contradiction"

Hotspot Engine

Observation Density + Contradictions + Customer Requests + Risk Trend + Business Value
  ↓
Hotspot Score → Mission Generator

Knowledge Gap

Area + Coverage + Confidence + Priority + Estimated Value + Budget
  ↓
Recommended Mission

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Intelligence Ledger

Separate from financial accounting:

Field	Description
Mission	Which mission
Budget	Credits allocated
Credits Reserved	Committed
Credits Consumed	Spent
Knowledge Produced	Observations created
Decision Produced	Verified decisions
Business Impact	Measured value
ROI	Return on investment

Questions answered:

• How many kronor did this verified Decision Case cost?
• Which municipality gives highest knowledge return per invested krona?

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Architecture Principles

1. Every component answers: What value is created here? Who pays for it?
2. Ontology before model — taxonomy answers "what does it mean?"
3. AI models trained on curated datasets, not whole archive
4. Knowledge gaps drive missions, not just customer orders
5. Contradictions are opportunities, not errors
6. Economic engine as important as AI models

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Related Documents

• ADR-011: Four-Layer Data Architecture
• DECISION_MODEL_v1.0.md
• EPIC-001-First-Verified-Decision.md