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Brando Implementation Service

The Brando Implementation Service is a full-lifecycle programme for converting your brand’s static guidelines into three tightly connected AI-native layers:

  1. Brand Directive Vocabulary (BDV) – the directive ontology layer that defines how the brand must behave inside AI systems.
  2. Machine-Actionable Policy Graph (MPG) – the executable governance layer that models guard rails, constraints, and behavioural logic.
  3. Brand Operating System (Brand OS) – the runtime that AI models, agents, and multimodal systems query to produce consistent, compliant, on-brand behaviour.

Brando transforms brand identity, tone, language, tokens, and governance into an enforceable linked-data system that modern AI can execute deterministically.

Many organisations have structured metadata (Schema.org, GS1, UNSPSC) describing what their products or brands are.

Brando adds the missing layer: a directive ontology (BDV) that governs how AI must behave.

Why this is different

Most “brand AI” work stops at:

  • prompts
  • tone decks
  • bespoke fine-tunes
  • verbal and visual guidelines

These provide influence, not control.

Brando provides control.

The Implementation Service creates a BDV-powered Policy Graph, which functions as the AI-native rulebook for brand behaviour. Instead of hoping a model “captures” your brand from loose cues, we give it:

  • formal rules,
  • linked constraints,
  • canonical vocabulary,
  • directive policies,
  • reusable Tokens (verbal, visual, audio),
  • and enforced context-aware behaviours.

This becomes an enterprise-grade Brand Operating System that governs every model, agent, and assistant.

Phase 1 — Map Your AI Brand Strategy

Description

Phase 1 uncovers the semantic and behavioural structure of your brand. AI systems understand meaning when it is expressed as linked data, so we identify the building blocks for the Brand Directive Vocabulary and the Machine-Actionable Policy Graph.

This is where we define:

  • identity,
  • values,
  • tone,
  • category semantics,
  • governance boundaries,
  • audience variation,
  • and contextual behaviour.

Actions

  • Analyse brand identity, tone, values, and narrative.
  • Map messaging, behaviours, personas, journeys, and intent structures.
  • Capture product taxonomies, GS1/Google alignments, and category semantics.
  • Identify behavioural constraints, regulated language, and refusal logic.
  • Build the semantic blueprint for the BDV and Policy Graph.

Outputs

  • A conceptual architecture for your brand as code.
  • Initial ontology and token maps.
  • A stable foundation for BDV design and policy modelling.
  • Cross-functional alignment (brand, CX, risk, compliance, product).

Phase 2 — Author Your Linked-Data Sets

Description

Phase 2 generates two tightly coupled layers:

1. Schema.org + GS1 Layer

Descriptive linked data modelling “what the brand and products are.”

2. Brando BDV Layer

Directive ontology modelling “how the brand must behave.”

BDV is defined using BrandoSchema’s classes:

  • Brando:VerbalIdentity
  • Brando:VisualIdentity
  • Brando:AudioIdentity
  • brando:Policy
  • brando:Context
  • brando:BrandedCategory
  • brando:SyntheticPersona
  • internally defined BDV extensions (if needed)

This becomes the backbone of the Machine-Actionable Policy Graph.

Actions

  • Build BDV (Brand Directive Vocabulary) tailored to the brand.
  • Codify tone, vocabulary, constraints, category language, refusal logic.
  • Encode rules, tokens, and policies in JSON-LD + YAML.
  • Align descriptive entities with Schema.org and GS1.
  • Create synthetic personas and context-aware behaviour rules.
  • Author the full Policy Graph and directive ontology.

Outputs

  • A comprehensive BDV: the world-model of your brand.
  • A fully authored Machine-Actionable Policy Graph.
  • Descriptive and directive layers aligned for AI execution.
  • Canonical JSON-LD and YAML ready for integration.

Phase 3 — AI-Native Brand Dress Rehearsal

Description

Now we load the BDV + Policy Graph into an AI Sandbox to see how models interpret and execute the directives.

We test:

  • tone,
  • personality,
  • constraints,
  • disallowed actions,
  • refusal strategies,
  • contextual shifts,
  • and cross-model consistency.

Actions

  • Load the BDV and Policy Graph into a sandbox environment.
  • Run structured tests across channels, tasks, and scenarios.
  • Evaluate outputs against tone, semantics, and governance rules.
  • Identify hallucinations, drift, or misinterpretations.
  • Refine BDV elements, tokens, and rules based on results.

Outputs

  • Verified and corrected AI behaviour.
  • A refined directive ontology and Policy Graph.
  • Confidence that runtime governance will work as expected.

Phase 4 — Deploy Your Linked-Data Architecture

Description

This is where the brand becomes a live, active AI runtime.

We deploy the Policy Graph and BDV into your systems so that AI agents, LLMs, and retrieval pipelines can query them for every generation.

Actions

  • Deploy BDV + Policy Graph into your environment.
  • Integrate with chatbots, RAG layers, agents, and APIs.
  • Activate governance enforcement at runtime.
  • Add schema validators and automated QA.
  • Test the Brand OS across journeys, products, and markets.

Outputs

  • Operational Brand Directive Vocabulary.
  • Enforced brand behaviour across all AI surfaces.
  • Single source of truth for identity, tone, language, and constraints.
  • Strongly governed generative and agentic systems.

Phase 5 — Maintain & Evolve Your Brand Graph

Description

A Brand OS must evolve as the brand evolves.

We maintain:

  • the BDV,
  • the Policy Graph,
  • category mappings,
  • regulated language,
  • and multi-market context variants.

Actions

  • Monitor AI outputs for drift or inconsistencies.
  • Update BDV and policies as brands and products change.
  • Introduce new contexts, personas, campaigns, or rules.
  • Synchronise with marketing, CX, risk, and governance teams.

Outputs

  • Continuously updated BDV and Policy Graph.
  • Stable behaviour even as models change.
  • Reduced AI performance decay.
  • Consistent global brand governance.

Phase 6 — Measure AI Brand Performance

Description

We evaluate how the BDV and Policy Graph perform in the wild.

Actions

  • Measure behavioural alignment, consistency, and tone compliance.
  • Assess regulated language adherence and safety performance.
  • Evaluate business impact, CX outcomes, and operational metrics.
  • Provide recommendations for optimisation and refinement.

Outputs

  • Measurable AI brand performance.
  • Clear ROI attribution.
  • Insights feeding the next optimisation cycle.
  • Brand Directive Vocabulary (BDV) — /types/
  • Brando Vocabulary (JSON-LD) — /spec/jsonld-context.md
  • YAML Schema Profile — /spec/yaml-profile.md
  • Brand OS Architecture — /architecture/brand-os.md
  • Runtime Integration — /architecture/runtime-integration.md
  • Examples & Blueprints — /examples/