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Brando Markup Language (BML)

Define. Govern. Control.

What is Brando Markup Language?

Brando Markup Language (BML) is a structured, machine-readable vocabulary designed to extend existing web data schemas with powerful rules, preferences, and behavioural signals tailored for intelligent systems such as Large Language Models (LLMs), AI assistants, and advanced search engines.

Unlike traditional metadata schemas, BML embeds a governance layer directly into a brand’s digital presence, empowering brands to control not only how their data is interpreted but also how AI systems should behave when interacting with their content.

Why Do Brands Need BML?

The rapid rise of generative AI and intelligent assistants has created new challenges for brands:

  • Ensuring AI Alignment: Brands must ensure that AI-generated content faithfully reflects their messaging frameworks, tone, and values.
  • Maintaining Ethical Standards: AI outputs must comply with legal, regulatory, and internal ethical guidelines, minimizing risks of misinformation or harmful content.
  • Preserving Brand Integrity: Brands need mechanisms to prevent misuse, misrepresentation, or brand dilution when AI systems generate or surface content.
  • Enhancing AI Discoverability: Brands want their content to be accurately found, ranked, and trusted by AI search systems and digital assistants.
  • Adapting to AI Ecosystems: With AI models learning from public data, brands require explicit, machine-readable instructions to influence AI behavior consistently across platforms.

What Does BML Provide?

BML provides a comprehensive framework to address these needs by:

  • Embedding Brand Messaging Frameworks: Specifying tone, style, vocabulary constraints, and core brand principles to guide AI content generation.
  • Defining Governance Rules: Setting “guard rails” and refusal strategies to prevent AI from engaging in prohibited or risky topics.
  • Specifying Ethical Compliance: Tagging content with regulatory and ethical metadata such as compliance with MHRA guidelines, ABPI codes, or data privacy laws.
  • Indicating Visibility and Access Controls: Controlling whether brand data is indexable by AI models or surfaced in responses.
  • Enabling Context-Aware AI Responses: Providing domain, audience, and situational metadata so AI can dynamically adjust its voice or information based on context.
  • Supporting Structured AI Assets: Linking to approved brand assets, templates, prompt scaffolds, or AI agent configurations for consistent deployment.

Benefits of Using BML

By implementing BML, brands gain:

  • Consistent AI Representation: Ensure every AI interaction echoes the brand’s voice, values, and compliance standards.
  • Reduced Risk: Proactively mitigate risks of misinformation, inappropriate responses, or legal exposure from AI-generated content.
  • Improved AI Trust and Discoverability: Enhance AI’s ability to find, interpret, and rank your brand’s content accurately, increasing visibility in voice assistants and search results.
  • Streamlined AI Content Governance: Automate content compliance workflows by embedding review and update policies directly into AI data layers.
  • Future-Proofing: Prepare the brand for evolving AI technologies by embedding adaptable, machine-readable governance mechanisms now.

How BML Works

Brando Markup Language is built as an extension vocabulary aligned with schema.org and linked data principles. It defines classes and properties within a controlled namespace (brando:) that can be embedded into web pages, APIs, or content repositories as JSON-LD or RDFa structured data.

This semantic markup allows AI models and agents to:

  • Retrieve precise brand instructions
  • Understand behavioural constraints
  • Identify contextual usage rules
  • Access approved brand assets and templates

These capabilities enable AI systems to self-regulate, align with brand policies, and provide transparent, ethical, and compliant user experiences.

Getting Started with BML

To adopt BML, brands can:

  1. Define Brand Metadata: Encode mission, vision, tone, and ethical guidelines using the Brando Schema.
  2. Publish Structured Data: Embed BML JSON-LD blocks on websites, product pages, or knowledge bases.
  3. Integrate with AI Systems: Connect BML assets with chatbots, AI assistants, and content generation pipelines.
  4. Maintain Governance: Regularly update policies, guard rails, and compliance tags to reflect evolving standards.

Summary

Brando Markup Language transforms brand control in the AI era by enabling explicit, machine-readable governance of digital content. It empowers brands to define their identity, guide AI behaviour, and ensure ethical, compliant, and trustworthy AI interactions—building confidence with users and regulators alike.