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Author	SHA1	Message	Date
Bernt	df5e2b3e78	docs: LandveX Intelligence Lab v1.2 — MVP + Phases + Data Quality + Decision Analytics - MVP Milestone: 'First Verified Decision' - Developer films with quiXzoom, imports to Lab, corrects AI, creates Decision Case, follows chain with full traceability - When this works = first complete verifiable Control Intelligence pipeline - Three development phases: Phase 1 (Essential): Ingestion, Dataset Explorer, Annotation, Decision Case Viewer Phase 2 (Scale): Replay, Benchmark, Evaluation Phase 3 (Advanced): GPU Jobs, Hyperparameter Runs, Model Promotion, Canary - Product Architecture: quiXzoom → Observations → Intelligence Lab → Improved Models → LandveX → Better Decisions → Feedback → Intelligence Lab - Two products: quiXzoom (observations), LandveX (decisions) - Intelligence Lab = the factory that improves both - New areas: - Data Quality: Healthy/Blurred/Duplicate/Wrong GPS/Night/Rain/Occluded + Coverage (Roads, Buildings, Signs, Drainage, Vegetation) - Decision Analytics: Acceptance Rate, Ignore Rate, Accuracy, Insufficient Evidence, Data Collection Value - Business value metrics, not traditional AI metrics Rationale: Build MVP first, prove first real workflow, then scale. Decision Cases are the heart. Data Quality explains model performance. Decision Analytics measure business value.	2026-07-02 12:27:16 +00:00
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
Bernt	f6119e7bb6	docs: LandveX Intelligence Lab specification v1.0 - Internal development environment for Control Intelligence - Core principle: 'Produces verified Control Intelligence, not AI models' - Separate repo: landvex-intelligence-lab - Navigation: Dashboard, Models, Datasets, Annotations, Training, Evaluation, Decision Cases, Replay, Validation, Deploy, Settings - Key features: - Dashboard: AI status (models, datasets, jobs, cases) - Mission Replay: click through entire chain - Annotation: video + AI suggestion + manual correction - Decision Cases: first-class objects, all playable - Benchmark: compare YOLO, Grounding DINO, SAM, custom models - Replay: find regressions between model versions - Validation: field trials, scenario tests, decision tests - Deploy: 'Promote Model' not 'Deploy' (dev → validation → pilot → prod) - Experiments: link EP-1.0, DS-001, etc. to real data - Target: New AI engineer understands in minutes: 'This is where we build, test, and verify LandveX Control Intelligence before anything reaches production.' Rationale: Single internal tool for all AI development. Centralizes model training, annotation, validation, replay, decision chains, regression tests, experiments, and model promotion.	2026-07-02 12:24:18 +00:00

Author

SHA1

Message

Date

Bernt

df5e2b3e78

docs: LandveX Intelligence Lab v1.2 — MVP + Phases + Data Quality + Decision Analytics

- MVP Milestone: 'First Verified Decision'
  - Developer films with quiXzoom, imports to Lab, corrects AI,
    creates Decision Case, follows chain with full traceability
  - When this works = first complete verifiable Control Intelligence pipeline

- Three development phases:
  Phase 1 (Essential): Ingestion, Dataset Explorer, Annotation, Decision Case Viewer
  Phase 2 (Scale): Replay, Benchmark, Evaluation
  Phase 3 (Advanced): GPU Jobs, Hyperparameter Runs, Model Promotion, Canary

- Product Architecture: quiXzoom → Observations → Intelligence Lab →
  Improved Models → LandveX → Better Decisions → Feedback → Intelligence Lab
  - Two products: quiXzoom (observations), LandveX (decisions)
  - Intelligence Lab = the factory that improves both

- New areas:
  - Data Quality: Healthy/Blurred/Duplicate/Wrong GPS/Night/Rain/Occluded
    + Coverage (Roads, Buildings, Signs, Drainage, Vegetation)
  - Decision Analytics: Acceptance Rate, Ignore Rate, Accuracy,
    Insufficient Evidence, Data Collection Value
    - Business value metrics, not traditional AI metrics

Rationale: Build MVP first, prove first real workflow, then scale.
Decision Cases are the heart. Data Quality explains model performance.
Decision Analytics measure business value.

2026-07-02 12:27:16 +00:00

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

Bernt

f6119e7bb6

docs: LandveX Intelligence Lab specification v1.0

- Internal development environment for Control Intelligence
- Core principle: 'Produces verified Control Intelligence, not AI models'
- Separate repo: landvex-intelligence-lab
- Navigation: Dashboard, Models, Datasets, Annotations, Training,
  Evaluation, Decision Cases, Replay, Validation, Deploy, Settings

- Key features:
  - Dashboard: AI status (models, datasets, jobs, cases)
  - Mission Replay: click through entire chain
  - Annotation: video + AI suggestion + manual correction
  - Decision Cases: first-class objects, all playable
  - Benchmark: compare YOLO, Grounding DINO, SAM, custom models
  - Replay: find regressions between model versions
  - Validation: field trials, scenario tests, decision tests
  - Deploy: 'Promote Model' not 'Deploy' (dev → validation → pilot → prod)
  - Experiments: link EP-1.0, DS-001, etc. to real data

- Target: New AI engineer understands in minutes:
  'This is where we build, test, and verify LandveX Control Intelligence
   before anything reaches production.'

Rationale: Single internal tool for all AI development. Centralizes
model training, annotation, validation, replay, decision chains,
regression tests, experiments, and model promotion.

2026-07-02 12:24:18 +00:00

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