---
language:
  - en
license: other
license_name: bws-rights-managed-dataset-license
license_link: https://github.com/bwphotography-arch/bwphotography-arch/blob/main/License.txt
pretty_name: "BWS Data Solutions: Anonymized Maid Service V1"
task_categories:
  - image-classification
  - image-segmentation
tags:
  - privacy-safe
  - enterprise-data
  - computer-vision
  - commercial
  - rights-managed
  - image-dataset
  - vlm-training
  - clear-act-2026
  - human-subject
  - fashion
  - multimodal
size_categories:
  - 1K<n<10K
---

# 🧹 BWS Maid Service: Compliance-Native Multimodal Tokens (V1)

> **🛡️ Engineering Evaluation Sandbox (Active 7-Day Access)**
> *   **Technical Ingestion Portal:** `s3://createphotos` (Whitelisted buckets only)
> *   **Secure Evaluation Link:** [Download BWS_Maid_Service_V1_Enterprise_POC.zip](https://createphotos.s3.us-east-2.amazonaws.com/11_Maid_Service_30_Enterprise_POC.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQG4WARWX4FT3GA4F%2F20260625%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20260625T132913Z&X-Amz-Expires=604800&X-Amz-SignedHeaders=host&X-Amz-Signature=8f977d64ae3d48521583c725450386f164420ce4ae8f34c80b766ecb9657f64b)
> *   **Direct Manifest Auditor:** [BWS Forensic Manifest Repository](https://github.com/bwphotography-arch/BWS-Forensic-Manifests-2026)
> *   **Procurement:** All assets are 2026 US CLEAR Act compliant. Access is granted to whitelisted engineering nodes only. Forward your AWS Account ID to info@brettwilliams.org for permanent production whitelist access.

This repository serves as the engineering evaluation sandbox for **Brett Williams Studios LLC**. It contains highly specialized, rights-managed visual training tokens capturing service industry roles, uniform drape geometries, and everyday professional spatial environments.

**Defusing the Historical Contract Bottleneck:** Captured prior to 2022—before modern AI foundation models existed—this archive utilizes an advanced **Total Face Neutralization** process. This provides your engineering and legal teams with an airtight hedge—completely removing biometric landmark arrays, Right of Publicity exposure, and CCPA/GDPR personal data liabilities, while fully preserving high-fidelity environmental context and uncompressed uniform textures.

## 📐 The Core S3 Bucket Architecture

We utilize a concrete three-tier cloud storage architecture for immediate pipeline integration:

* **1. Preview Node (`s3://createphotos`):** Staged with evaluation zip packages containing dedicated uniform-industry metadata manifests.
* **2. Secured Engine (`s3://bws-delivery-payloads-2026`):** Staged with production-grade service industry RAW assets, block-buffered forensic track files, and master parquet manifests.
* **3. Delivery Pipeline (`s3://master-sales`):** Folders 01–18 designed for custom enterprise drops via time-limited 7-day presigned whitelist links.

## ⚖️ Legal Architecture & Risk De-Risking

* **Federal Regulatory Alignment:** Fully optimized to satisfy the **2026 US CLEAR Act** reporting mandates and state-level disclosure rules (**AB 2013**). Delivered with automated, block-buffered SHA-256 forensic tracking logs.
* **Absolute Chain of Title:** 100% proprietary, creator-owned source captures. Absolute copyright authority is held under **17 U.S.C. § 106** (US Copyright Reg: **VA 2-196-154**). Zero scraped or gray-market data.

## 💎 Commercial Pricing & Procurement

We operate on transparent, high-velocity enterprise pricing to accelerate your model training cycles. To request a formal Statement of Work (SOW), trigger a validation invoice, or whitelist your engineering team's AWS Account ID for secure cross-account bucket access, contact our procurement node:

**info@brettwilliams.org**  
*(Please include your organization name, intended VLM/spatial use case, and requested deployment scale).*

## 🚀 Quick Start Evaluation

```python
from datasets import load_dataset

dataset = load_dataset("BWS-Data-Solutions/11-Maid-Service-30-Enterprise-CLEAR-Compliant-POC")

# View a service industry token sample
example = dataset["train"][0]
example["image"].show()