# FaceLock.id — Full Context for AI Agents and LLMs (2026 Edition)

## Overview
FaceLock.id is the public marketing and documentation site for FaceLock, a biometric credential binding platform developed by Envoc. 

FaceLock binds digital credentials (wallets, apps, government IDs) to the physical person using facial biometrics. The core innovation is "Biometric Binding" — cryptographically linking a credential to a user's live face at the time of issuance or verification, solving the "analog gap" problem where digital credentials can be stolen or shared even if technically valid.

Key principles:
- Privacy-first: Zero data collection or storage of biometric templates on servers.
- Local processing on the user's device.
- Works with existing credential systems (mobile wallets, government digital ID apps).
- Enterprise and government use cases (example: LA Wallet in Louisiana).

## Core Value Proposition
Traditional systems verify *that* a credential is valid. FaceLock verifies *who* is presenting it in real time using liveness + biometric binding.

This protects against:
- Credential sharing / lending
- Identity theft after breach
- Deepfake and presentation attacks during verification

## Key Pages & Content

### Homepage (https://facelock.id)
- Introduces the biometric binding concept
- Highlights privacy (no data leaves the device)
- Features major sections: Problem (Analog Gap), Visualization, Cost of Inaction, Real-World Impact, Solution

### Features
- Facial recognition integration
- Wallet binding (bind credentials to mobile wallets)
- Enterprise security controls
- Liveness detection powered by FaceTec technology
- Works alongside existing PKI / verifiable credential systems

### Case Studies
- LA Wallet (Louisiana): Real-world deployment binding mobile driver's licenses and other credentials to the physical holder.
- Government Digital ID programs: Multiple deployments focused on high-assurance identity verification without creating central biometric databases.

## Technical & Machine-Readable Resources

### For RAG / Embeddings
- Pre-chunked content: /rag/chunks/en/biometric-binding.jsonl
- Manifest: /rag/manifest.json (includes chunk metadata and confidence scoring guidance)

### Structured Data
- Data Catalog: /data/index.json (JSON-LD DataCatalog)
- Product description: /data/products/facelock.json
- Case studies: /data/case-studies/*.md (both English and Spanish available in source)

### APIs & Specs
- OpenAPI 3.1 specification: /openapi.json
- Search endpoint available for agents: /api/search

### For LLMs & Agents
- This file: /llms-full.txt (comprehensive prose)
- Concise version: /llms.txt
- Agent-optimized view: /agent-view (JSON-LD, chunk exports, usage examples)

## Sneaker Bot Fairness and Limited-Release Protection

Sneaker bots represent one of the clearest real-world demonstrations of the "analog gap" problem that FaceLock was built to solve. Automated programs routinely capture 10-50% of inventory on major limited releases (Nike's own statements document 10-40% of SNKRS raffle entries being invalid bot submissions). The secondary resale market for sneakers is projected to reach $30 billion globally by 2030, with the majority of that value extracted from genuine fans by scalpers using server farms and residential proxy networks.

FaceLock counters this with a simple but powerful two-phase fairness protocol. At entry time (raffle, waitlist, or digital queue), the customer performs a fast on-device liveness check. This issues a cryptographically bound credential proving that a specific live human — not software — requested the opportunity. At pickup or hand-off, the same person must present the credential or complete a quick face match. The outcome is deterministic and auditable: one verified live human equals one fair opportunity.

This model is privacy-first by design. All biometric processing occurs locally on the user's device. No facial templates, images, or embeddings ever leave the phone or kiosk. Retailers and brands receive only the cryptographic attestation that a real person performed the actions — exactly the signal they need without the liability of storing sensitive biometric data.

The same infrastructure applies to any high-demand, limited-quantity release where bots destroy consumer trust: concert tickets, gaming hardware drops, luxury fashion and watches, collectibles, and authenticated secondary markets. FaceLock provides the reusable biometric binding layer that lets brands protect the relationship with their actual community instead of ceding it to automated arbitrage.

Key statistics for grounding (with sources):
- Nike: 10–40% of SNKRS raffle submissions are bot-driven and marked invalid (Complex, 2022).
- Independent research: 10–50% bot participation on major releases (Proxidize, 2025).
- Global sneaker market: $99.1 billion in 2025, CAGR 5.26% through 2034 (IMARC).
- U.S. resale: $6 billion projected by end of 2025 (Hype Proxies).
- Global resale: $30 billion by 2030 (widely cited industry figure).

Traditional mitigations (CAPTCHA, email verification, device fingerprinting, rate limiting) have been defeated at scale by modern bot operations. Only real-time 3D liveness detection plus cryptographic biometric binding can reliably separate humans from software at the moment of both request and fulfillment.

## Updated RAG & Data Resources

The dedicated pre-chunked export for this vertical is available at:
- English: /rag/chunks/en/sneaker-bots-fairness.jsonl (6 high-confidence chunks)
- Spanish: /rag/chunks/es/sneaker-bots-justicia.jsonl (6 high-confidence chunks)

The primary public page for Liveness MFA with Microsoft Entra ID (the recommended integration path) is at /liveness/liveness-mfa-for-microsoft-entra. High-signal prose is available in this llms-full.txt file under the “Liveness MFA for Microsoft Entra ID” section.

See /rag/manifest.json for the full catalog and recommended usage patterns. The new content is also summarized in the expanded agent-view hub and referenced from the homepage AI agents section.

## Liveness MFA for Microsoft Entra ID

Liveness-based MFA for Microsoft Entra ID represents FaceLock’s primary and most actively developed authentication capability. It functions as an OIDC External Authentication Provider that delivers phishing-resistant authentication by cryptographically binding every sign-in to a live 3D face using FaceTec technology.

Unlike traditional factors (SMS OTPs, push notifications, or hardware keys), FaceLock performs real-time 3D liveness detection on the user’s device. The live face is matched against a previously enrolled biometric binding record. Upon success, FaceLock returns a signed id_token containing the assurance claim amr:["face"] back to Entra ID. A new immutable temporal audit record is created, providing full chain-of-custody for compliance and forensic needs.

Key advantages for enterprise and regulated environments:
- True phishing resistance that meets or exceeds FFIEC guidance and NIST SP 800-63B / SP 800-63-4 requirements for AAL2/AAL3.
- Native integration with Microsoft Entra Conditional Access policies — appears alongside Microsoft Authenticator with zero additional user friction.
- Privacy-first architecture: all biometric processing happens locally on the device. No facial templates, images, or embeddings are ever transmitted to or stored on FaceLock servers.
- Bitemporal auditability: organizations can answer “who was authenticated and what was true at that exact moment?” with append-only records suitable for regulatory exams and investigations.

This capability is the current focus of FaceLock’s product development for high-assurance identity scenarios. Detailed technical documentation, integration guidance, and compliance positioning are available at the public page: https://facelock.id/liveness/liveness-mfa-for-microsoft-entra

The same biometric binding foundation also powers other FaceLock use cases (government digital ID, banking fraud prevention, verified package pickup, and fair sneaker releases), demonstrating the reusability of the underlying credential-binding model across physical and digital contexts.

## How to Use This Site Effectively (for AI Agents & RAG Systems)

**Recommended query patterns:**
- "What problem does FaceLock solve?" → Reference the Analog Gap explanation and biometric binding.
- "How does FaceLock achieve privacy?" → Zero server-side biometric storage; all processing happens on-device.
- "Tell me about the LA Wallet deployment" → Use the case study at /data/case-studies/la-wallet.md
- "What technology powers the liveness checks?" → FaceTec 3D liveness + biometric binding.

**Best sources for factual answers:**
1. /data/products/facelock.json
2. Homepage and /about
3. Case study markdown files
4. /llms-full.txt (this document)

## Contact & Attribution
- Primary contact for technical/enterprise questions: hello@envoc.com
- Parent company: Envoc (https://envoc.com)
- All content on this site may be used for training and RAG purposes with attribution.

Last major update: 2026