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Bernt 9a817ac82a docs: LandveX Intelligence Lab v1.1 — Architecture + Pipelines + Immutability

- 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.

2026-07-02 12:26:00 +00:00

8.1 KiB

Raw Blame History

LANDVEX INTELLIGENCE LAB

Internal Development Environment for Control Intelligence


Version	1.0
Status	SPECIFICATION
Purpose	Build, test, and verify LandveX Control Intelligence before production

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

landvex-intelligence-lab

Separate from:

quixzoom-app
landvex-web
aamos-core

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

Dashboard
├── Models
├── Datasets
├── Annotations
├── Training
├── Evaluation
├── Decision Cases
├── Replay
├── Validation
├── Promote Model
└── Settings

Dashboard

Shows AI status, not business data.

┌─────────────────────────────────────┐
│ Datasets                            │
│ Healthy: 12                         │
│ Needs Review: 3                     │
│ Corrupted: 0                        │
├─────────────────────────────────────┤
│ Training Jobs                       │
│ Running: 2                          │
│ Queued: 1                           │
│ Completed: 47                       │
│ Failed: 0                           │
├─────────────────────────────────────┤
│ Decision Cases                      │
│ Validated: 23                       │
│ Pending: 5                          │
│ Rejected: 2                         │
├─────────────────────────────────────┤
│ Replay Jobs                         │
│ Ready: 8                            │
│ Running: 1                          │
│ Finished: 34                        │
└─────────────────────────────────────┘

Mission Replay

Click through the entire chain:

Video → Frame → Bounding boxes → Detected objects → Evidence → Finding → Decision → Business Impact

Annotation

┌─────────┬─────────────┬──────────────┐
│ Video   │ AI Suggestion│ Manual       │
│         │              │ Correction   │
├─────────┼─────────────┼──────────────┤
│         │ Object:      │ Correct?     │
│         │ Road Crack   │ YES / NO     │
│         │ Confidence:  │              │
│         │ 82%          │ Severity:    │
│         │              │ Low / Medium │
│         │              │ / High       │
└─────────┴─────────────┴──────────────┘

Decision Cases

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
├── Reality
├── Observation
├── Evidence
├── Finding
├── Decision
├── Outcome
└── Learning

All cases playable.

Benchmark

Compare models:

Model	Precision	Recall	F1	Latency	Decision Accuracy
YOLO v8	0.89	0.87	0.88	45ms	—
Grounding DINO	0.91	0.85	0.88	120ms	—
SAM	0.88	0.90	0.89	200ms	—
Custom	0.92	0.91	0.915	60ms	0.87

Replay

Find regressions:

Mission 213
├── Play
├── Show AI
├── Show Human Annotation
├── Differences
├── New Model
└── Old Model

Validation

Field Trials
Scenario Tests
Decision Tests
Evidence Tests
Golden Failures
Regression Tests

Promote Model (Not Deploy)

Development → Validation → Pilot → Production

Not "Deploy". "Promote Model".

Experiments

Experiments
├── EP-1.0
├── DS-001
├── DS-002
├── DS-003
├── Field Trials
└── Metrics

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
Annotation
Datasets
Replay
Decision chains
Validation
Regression tests
Experiments
Model promotion

New Developer Experience

A new AI engineer should open the repo and within minutes understand:

"This is the tool where we build, test, and verify LandveX Control Intelligence before anything reaches production."

Relationship to Principles

All development in Git
All experiments reproducible
All models traceable from training to validation to production
Version control and traceability

ändringshistoria

Version	Datum	Beskrivning
1.0	2026-07-02	Initial specification for LandveX Intelligence Lab

STATUS

SPECIFICATION — Awaiting development decision

8.1 KiB Raw Blame History