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docs: LandveX Intelligence Lab v1.1 — Architecture + Pipelines + Immutability

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- Added architecture diagram:
  - Developer UI / Training API / Experiment API
  - Event Bus / Queue
  - Storage Layer (S3/R2 + PostgreSQL + Neo4j)
  - Processing Pipelines

- Seven separate pipelines (not one big AI loop):
  1. Ingestion — raw images/video/GPS/EXIF → Raw Dataset
  2. Dataset — sort, deduplicate, quality control → Validated Dataset
  3. Annotation — AI suggestions, manual correction → Verified Dataset
  4. Training — hyperparameters, checkpoints, GPU → Model Artifact
  5. Evaluation — precision, recall, F1, decision accuracy → Report
  6. Replay — compare model versions → Regression Report
  7. Decision Validation — full chain → Validation Report

- Bucket structure: raw-images, raw-video, missions, datasets,
  annotations, training, models, replays, evaluation,
  decision-cases, field-trials, exports, archives

- AI Job Queue: all jobs asynchronous (Upload → Queue → Worker → GPU → Storage → Notification)

- Core principles updated:
  - All artifacts immutable and versioned
  - Every change traceable to experiment, model, dataset, decision

- Dashboard shows: Datasets (Healthy/Needs Review/Corrupted),
  Training Jobs, Decision Cases, Replay Jobs

- Decision Cases emphasized as most important asset

Rationale: Separate pipelines make system easier to debug, improve,
and swap components. Immutability aligns with E-001 Git/traceability
principles. Decision Cases become the unique asset over time.
This commit is contained in:
Bernt
2026-07-02 12:26:00 +00:00
parent f6119e7bb6
commit 9a817ac82a
1 changed files with 141 additions and 14 deletions
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docs/design/LANDVEX_INTELLIGENCE_LAB.md
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@@ -10,12 +10,22 @@
---
## Core Principle
## Core Principles
**LandveX Intelligence Lab does not produce AI models. It produces verified Control Intelligence.**
This is an internal tool. Never a customer product.
**All artifacts are immutable and versioned.**
- Raw images are never modified
- Annotations are versioned
- Models are versioned
- Evaluation reports are versioned
- Decision Cases are versioned
- Replay results are saved as new artifacts
**Every change must be traceable to a specific experiment, model version, dataset, and decision.**
---
## Repository
@@ -31,6 +41,28 @@ Separate from:
---
## Architecture
```
LandveX Intelligence Lab
│
┌─────────────────┼─────────────────┐
│ │ │
Developer UI Training API Experiment API
│ │ │
└─────────────────┴─────────────────┘
│
Event Bus / Queue
│
─────────────────────────────────────────────────────
Storage Layer
Images │ Videos │ Missions │ Models │ Logs
S3/R2 Buckets + PostgreSQL + Neo4j
─────────────────────────────────────────────────────
│
Processing Pipelines
```
## Navigation
```
@@ -43,7 +75,7 @@ Dashboard
├── Decision Cases
├── Replay
├── Validation
├── Deploy (Promote Model)
├── Promote Model
└── Settings
```
@@ -55,26 +87,26 @@ Shows AI status, not business data.
```
┌─────────────────────────────────────┐
│ Models │
│ Detection v12 │
│ Segmentation v5 │
│ OCR v3 │
│ Decision Model v1.0 │
├─────────────────────────────────────┤
│ Datasets │
│ Roads, Buildings, Signs │
│ Vegetation, Drainage │
│ Healthy: 12 │
│ Needs Review: 3 │
│ Corrupted: 0 │
├─────────────────────────────────────┤
│ Training Jobs │
│ Running: 2 │
│ Queued: 1 │
│ Failed: 0 │
│ Completed: 47 │
│ Failed: 0 │
├─────────────────────────────────────┤
│ Decision Cases │
│ Validated: 23 │
│ Pending: 5 │
│ Rejected: 2 │
├─────────────────────────────────────┤
│ Replay Jobs │
│ Ready: 8 │
│ Running: 1 │
│ Finished: 34 │
└─────────────────────────────────────┘
```
@@ -108,9 +140,19 @@ Video → Frame → Bounding boxes → Detected objects → Evidence → Finding
---
## Decision Case
## Decision Cases
First-class objects:
**The most important asset.**
Not images. Not videos. Not AI models.
But:
```
Observation → Evidence → Finding → Decision → Outcome → Learning
```
After a few years, hundreds of thousands of verified Decision Cases.
Not just a training dataset — a library of how real observations lead to real decisions and real outcomes.
```
Case #4232
@@ -169,7 +211,7 @@ Regression Tests
---
## Deploy (Promote Model)
## Promote Model (Not Deploy)
```
Development → Validation → Pilot → Production
@@ -195,6 +237,91 @@ Link experiment protocol to real development and validation data.
---
## Processing Pipelines
### 1. Ingestion Pipeline
**Input:** Images, video, GPS, EXIF, metadata
**Checks:** Checksums, versioning
**Output:** Raw Dataset
### 2. Dataset Pipeline
**Input:** Raw Dataset
**Checks:** Sort, deduplicate, quality control, resolution, blur detection, GPS validation
**Output:** Validated Dataset
### 3. Annotation Pipeline
**Input:** Validated Dataset
**Process:** AI suggestions, manual correction, label versions, consensus
**Output:** Verified Dataset
### 4. Training Pipeline
**Input:** Verified Dataset
**Process:** Start training, hyperparameters, checkpoints, GPU jobs
**Output:** Model Artifact
### 5. Evaluation Pipeline
**Input:** Model Artifact
**Metrics:** Precision, recall, F1, decision accuracy, regression
**Output:** Evaluation Report
### 6. Replay Pipeline
**Input:** Old missions, Model v14, Model v15
**Process:** Run both models, compare differences
**Output:** Regression Report
### 7. Decision Validation Pipeline
**Input:** Observation
**Process:** Full chain — Observation → Evidence → Finding → Decision → Business Impact
**Output:** Decision Validation Report
---
## Bucket Structure
```
raw-images/
raw-video/
missions/
datasets/
annotations/
training/
models/
replays/
evaluation/
decision-cases/
field-trials/
exports/
archives/
```
All content is versioned:
```
model-v14/
model-v15/
model-v16/
```
---
## AI Job Queue
All jobs are asynchronous:
```
Upload → Queue → Worker → GPU → Storage → Notification
```
Not synchronous API calls.
---
## What This Tool Collects
- Model training