platform-admin/boc
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
42 Commits 1 Branch 0 Tags
67ca05f2b2ef0e1917ffabb69bbd469edda38da2
Commit Graph

5 Commits

Author SHA1 Message Date
Bernt 67ca05f2b2 docs: LandveX Intelligence Lab v1.3 — Government Quality Standards added 
- Added comprehensive quality standards for public sector:
  - Code Quality: TypeScript/Rust strict, ≥80% test coverage, static analysis,
    mandatory code review, API documentation
  - Security: OAuth 2.0 + MFA, RBAC + audit, AES-256/TLS 1.3,
    HashiCorp Vault, weekly vulnerability scans
  - Audit & Compliance: Immutable signed logs, configurable retention,
    full export, WCAG 2.1 AA, Swedish + English
  - Infrastructure: GitOps, OpenTelemetry, 3-2-1 backup, RPO<1h RTO<4h,
    horizontal scaling
  - AI/ML: MLflow versioning, full data lineage, bias testing,
    SHAP/LIME explainability, model cards
  - Development: Git workflow, CI/CD, IaC, dependency management,
    incident response runbooks

Rationale: LandveX serves municipalities and government agencies.
Intelligence Lab must satisfy public sector procurement, audit,
and compliance requirements from day one.
 2026-07-02 13:08:22 +00:00
Bernt 8e9871f209 docs: LandveX Intelligence Lab — Government Quality Standards 
- Added comprehensive quality standards for public sector:
  - Code Quality: TypeScript/Rust strict, ≥80% test coverage, static analysis,
    mandatory code review, API documentation
  - Security: OAuth 2.0 + MFA, RBAC + audit, AES-256/TLS 1.3,
    HashiCorp Vault, weekly vulnerability scans
  - Audit & Compliance: Immutable signed logs, configurable retention,
    full export, WCAG 2.1 AA, Swedish + English
  - Infrastructure: GitOps, OpenTelemetry, 3-2-1 backup, RPO<1h RTO<4h,
    horizontal scaling
  - AI/ML: MLflow versioning, full data lineage, bias testing,
    SHAP/LIME explainability, model cards
  - Development: Git workflow, CI/CD, IaC, dependency management,
    incident response runbooks

Rationale: LandveX serves municipalities and government agencies.
Intelligence Lab must satisfy public sector procurement, audit,
and compliance requirements from day one.
 2026-07-02 12:49:26 +00:00
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