LVMH Signals

The world's largest luxury goods conglomerate headquartered in Paris, operating over 75 prestigious brands. Portfolio includes Louis Vuitton, Christian Dior, Moët & Chandon, Hennessy, Sephora, and Tiffany & Co. spanning fashion, wine, cosmetics, and jewelry.

This is a Naftiko Signals read of LVMH’s technology and AI investment posture — mined from public workforce signals (job postings, press releases, newsroom content) and matched against Naftiko’s curated vocabulary of services, tools, concepts, and standards across the 44 Naftiko signal groups.

For LVMH we detected 39 areas, 6 services, 0 tools, and 3 standards — producing a total signal score of 20.

How to read this page: Impact Report is the narrative read-out. Capabilities are Naftiko agent workflows you could ship on top of LVMH’s existing stack — each one runnable in the Naftiko Framework. Navigation ranks the next moves we’d recommend, grouped by theme. Signals is the score breakdown across every Naftiko signal group, each row linking to its definition. Areas, Services, Tools, and Standards are the raw detections behind the score. Why? explains the methodology and audience.

Take Control Of Your Signals — Become a Naftiko Design Partner Today!

What’s next for you? Pick your role — we’ll point you at the right capability and next step.

LVMH Technology Investment Impact Report

Prepared by Naftiko

April 2026

Executive Summary

This report presents a signal-based analysis of LVMH’s technology investment posture, derived from Naftiko’s methodology of examining services deployed, tools adopted, concepts referenced, and standards followed across workforce signals. The analysis spans foundational infrastructure, data platforms, customization capabilities, operational efficiency, productivity, integration, governance, economics, and strategic alignment.

The world’s largest luxury goods conglomerate headquartered in Paris, operating over 75 prestigious brands. Portfolio includes Louis Vuitton, Christian Dior, Moët & Chandon, Hennessy, Sephora, and Tiffany & Co. spanning fashion, wine, cosmetics, and jewelry.

The assessment yields a total signal score of 20 across 41 dimensions. The six strongest signal categories are services (6), partnershipsEcosystems (3), governance (2), databases (1), openSource (1), standardization (1).

Layer 1: Foundational Layer

Evaluating LVMH’s foundational capabilities across Artificial Intelligence, Cloud, Open-Source, Languages, and Code — the core infrastructure and development building blocks supporting Retail / Fashion & Apparel and Consumer Goods / Personal Care & Household Products operations.

Waves: Large Language Models (LLMs), Generative Pre-trained Transformer (GPT), Open-Source LLMs

Artificial Intelligence — Score: 0 #69/72

LVMH’s AI score of 0 reflects adoption across limited recorded signals.

AI posture sets the ceiling on downstream AI workloads at LVMH.

Cloud — Score: 0 #69/72

Cloud score of 0 spans limited recorded signals alongside IaC tooling (limited recorded signals).

Concepts: Distributed

Cloud maturity is the anchor for scaling AI workloads at LVMH.

Open-Source — Score: 1 #52/72

Open-Source score of 1 anchored on limited recorded signals.

Consumption is broad; formal contribution posture is an opportunity area.

Languages — Score: 0 #70/72

Languages score of 0 across limited recorded languages.

Language mix is a revealing signal of LVMH’s engineering history and modernization priorities.

Code — Score: 0 #71/72

Code score of 0 anchored on limited recorded signals.

Developer tooling is the lever for compound productivity via AI coding assistants.

Layer Summary: LVMH’s Foundational Layer reflects AI (0), Cloud (0), Open-Source (1), Languages (0), and Code (0) — the bedrock on which every subsequent AI-readiness capability is built.

Layer 2: Retrieval & Grounding

Evaluating LVMH’s data and retrieval capabilities — Data, Databases, Virtualization, Specifications, Context Engineering — that ground AI workloads in enterprise information.

Waves: Vector Databases, Retrieval-Augmented Generation (RAG), Prompt Engineering, Context Engineering

Data — Score: 0 #70/72

Data score of 0 reflects limited recorded signals.

Analytical depth is the foundation for retrieval-augmented AI.

Databases — Score: 1 #52/72

Databases score of 1 spans limited recorded signals.

Portfolio rationalization is an ongoing lever.

Virtualization — Score: 0 #66/72

Virtualization score of 0 on limited recorded signals.

Containerization paired with VMs is a pragmatic stance.

Specifications — Score: 0 #58/72

Specifications score of 0 rooted in REST/HTTP/JSON.

Standards: HTTP

Formalizing OpenAPI as enterprise standard is a prerequisite for AI agent integration.

Context Engineering

LVMH’s Context Engineering investment is emerging.

Pairing strong Data with formal context layer is the highest-leverage AI readiness move.

Layer Summary: LVMH’s Retrieval posture is shaped by Data (0), Databases (1), and Specifications (0).

Layer 3: Customization & Adaptation

Evaluating LVMH’s capabilities for customizing AI to its business — Data Pipelines, Model Registry, Multimodal Infrastructure, Domain Specialization.

Waves: Fine-Tuning & Model Customization, Multimodal AI

Data Pipelines — Score: 1 #44/72

Data Pipelines score of 1 reflects limited recorded signals.

Streaming pipelines prerequisite for real-time AI inference.

Model Registry & Versioning — Score: 0 #64/72

Model Registry score of 0 via limited recorded signals.

Enterprise registry with approvals is prerequisite for regulated AI.

Multimodal Infrastructure — Score: 0 #56/72

Multimodal score of 0 via limited recorded signals.

Centralized OCR/audio/image extractors improve AI project productivity.

Domain Specialization — Score: 0

Domain Specialization score of 0.

Industry-specific fine-tuning is a differentiated long-term capability for LVMH.

Layer Summary: Customization bridges generic AI and business-specific advantage.

Layer 4: Efficiency & Specialization

Evaluating LVMH’s operational efficiency and platform layer — Automation, Containers, Platform, Operations.

Waves: Small Language Models (SLMs), Model Routing / Orchestration, Reasoning Models

Automation — Score: 0 #68/72

Automation score of 0 reflects Make.

Services: Make

Automation footprint is ready for AI augmentation on high-toil processes.

Containers — Score: 0 #67/72

Containers score of 0 on limited recorded signals.

Container-native AI deployment is the logical next step.

Platform — Score: 0 #68/72

Platform score of 0 spans limited recorded signals.

Platform engineering maturity is the next milestone.

Operations — Score: 0 #70/72

Operations score of 0 anchored on limited recorded signals.

Operational tooling is a foundation for AI-driven SRE.

Layer Summary: Automation (0) and Operations (0) are direct levers for AI-augmented productivity.

Layer 5: Productivity

Evaluating LVMH’s productivity tooling — SaaS, Services, Code.

Waves: Coding Assistants, Copilots

Software As A Service (SaaS) — Score: 0 #64/72

SaaS score of 0 includes Microsoft Teams.

Services: Microsoft Teams

Rationalization and unified auth are the optimization opportunities.

Code — Score: 0 #71/72

Code anchored on limited recorded signals.

Wiring coding assistants creates compound productivity gains.

Services — Score: 6 #57/72

Services score of 6 spans LVMH’s full service portfolio.

Services: Adobe Launch, Google Drive, Make, Microsoft Teams, Microsoft Windows 10, WebSphere

Scale of services drives the integration-layer imperative.

Layer Summary: Services score of 6 reflects LVMH’s breadth of enterprise SaaS consumption.

Layer 6: Integration & Interoperability

Evaluating LVMH’s API, integration, event-driven connectivity.

Waves: MCP (Model Context Protocol), Agents, Skills

API — Score: 0 #67/72

API score of 0; concepts: limited recorded signals.

Standards: HTTP

Formalizing API standards is the most impactful integration move.

Integrations — Score: 0 #69/72

Integrations score of 0 spans limited recorded signals.

iPaaS reduces integration debt.

Event-Driven — Score: 0 #65/72

Event-Driven score of 0.

Concepts: Events

Event streaming is foundational for real-time AI.

Patterns — Score: 0 #68/72

Patterns score of 0 on limited recorded signals.

Microservice patterns support AI workload scaling.

Specifications — Score: 0 #58/72

Specifications score of 0.

Standards: HTTP

Elevating OpenAPI to enterprise standard creates shared vocabulary for AI.

Apache — Score: 0 #69/72

Apache score of 0: limited recorded signals.

Apache ecosystem depth is a characteristic enterprise strength.

CNCF — Score: 0 #67/72

CNCF score of 0: limited recorded signals.

CNCF maturity pairs with Kubernetes adoption.

Layer Summary: API (0) and Integrations (0) are the connective tissue for scaling AI.

Layer 7: Statefulness

Evaluating LVMH’s Observability, Governance, Security, Data handling.

Waves: Memory Systems

Observability — Score: 0 #68/72

Observability score of 0 spans limited recorded signals.

Consolidation lowers cost.

Governance — Score: 2 #58/72

Governance score of 2; standards: limited recorded signals.

Extending to AI governance is the natural evolution.

Security — Score: 1 #62/72

Security score of 1 anchored on limited recorded signals.

AI-specific security is a required add to existing program.

Data — Score: 0 #70/72

Data — limited recorded signals anchors.

Continued cataloging and lineage pays off at AI scale.

Layer Summary: Observability (0), Governance (2), Security (1) reflect LVMH’s operational maturity.

Layer 8: Measurement & Accountability

Evaluating LVMH’s measurement discipline — Testing, Observability, DevEx, ROI.

Waves: Evaluation & Benchmarking

Testing & Quality — Score: 0 #68/72

Testing score of 0 with limited recorded signals.

AI-specific evals extend traditional testing.

Observability — Score: 0 #68/72

Observability — limited recorded signals.

Instrumented for AI workload observation.

Developer Experience — Score: 1 #61/72

DevEx score of 1 on limited recorded signals.

DORA metrics and IDPs are next investments.

ROI & Business Metrics — Score: 0 #71/72

ROI score of 0 reflects limited recorded signals.

ROI discipline translates naturally to AI initiatives.

Layer Summary: Measurement is necessary for accountable AI initiatives.

Layer 9: Governance & Risk

Evaluating LVMH’s Regulatory, AI Review, Security, Governance, Privacy controls.

Waves: Governance & Compliance

Regulatory Posture — Score: 1 #57/72

Regulatory score of 1 with standards: limited recorded signals.

Proactive alignment with NIST AI RMF is a forward investment.

AI Review & Approval — Score: 0 #68/72

AI Review score of 0 with limited recorded signals.

Formalized AI review council is the priority governance investment.

Security — Score: 1 #62/72

Security — limited recorded signals.

AI threat model coverage extends existing security.

Governance — Score: 2 #58/72

Governance standards: limited recorded signals.

AI governance as first-class discipline is the next step.

Privacy & Data Rights — Score: 0 #62/72

Privacy score of 0.

Formal ‘training-eligible?’ classification prevents inadvertent PII use in models.

Layer Summary: Security (1), Governance (2), AI Review (0) shape the AI compliance envelope.

Layer 10: Economics & Sustainability

Evaluating LVMH’s AI FinOps, Provider Strategy, Partnerships, Talent, Data Centers.

Waves: Cost Economics & FinOps, Supply Chain & Dependency Risk, Data Centers

AI FinOps — Score: 0 #61/72

AI FinOps score of 0 with limited recorded signals.

Token-cost tagging required before AI spend accelerates.

Provider Strategy — Score: 0 #64/72

Provider Strategy score of 0 with Microsoft Teams, Microsoft Windows 10.

Services: Microsoft Teams, Microsoft Windows 10

Explicit provider strategy tightens AI partnership decisions.

Partnerships & Ecosystem — Score: 3 #52/72

Partnerships score of 3 with Google Drive, Microsoft Teams, Microsoft Windows 10.

Services: Google Drive, Microsoft Teams, Microsoft Windows 10
Concepts: Ecosystems

AI-specific partnerships are emerging.

Talent & Organizational Design — Score: 1 #57/72

Talent score of 1 with limited recorded signals.

Concepts: Human Resources, Learning, Recruitings

Aligning talent toward AI skill development is direct leverage.

Data Centers

Data Centers investment signals are limited.

Hybrid-cloud vs public-cloud becomes a board-level question at AI scale.

Layer Summary: Talent (1) and Partnerships (3) most affect AI outcomes.

Layer 11: Storytelling & Entertainment & Theater

Evaluating LVMH’s strategic narrative — Alignment, Standardization, M&A, Experimentation.

Waves: Moltbook, Gastown, Ralph Wiggum, OpenClaw / Clawdbot, Artificial General Intelligence (AGI)

Alignment — Score: 0 #65/72

Alignment score of 0; concepts: limited recorded signals.

Aligning AI to business outcomes makes AI accountable.

Standardization — Score: 1 #54/72

Standardization score of 1: limited recorded signals.

Extending to AI (model cards, eval suites) is a natural evolution.

Mergers & Acquisitions — Score: 1 #57/72

M&A score of 1.

AI-capability acquisitions can accelerate gaps.

Experimentation & Prototyping

Experimentation & Prototyping signals are limited.

A named internal AI sandbox reduces shadow-AI risk.

Layer Summary: Alignment (0) and Standardization (1) shape AI program direction.

Strategic Assessment

Top Three 2026 Investment Recommendations for LVMH

Establish an API-First Integration Layer. API score of 0 across 6 services leaves LVMH without reusable connective tissue. Prioritize API standards, deploy an enterprise gateway, publish a service catalog.
Stand Up a Formal AI Governance and Review Council. AI Review score of 0 combined with AI adoption (score 0) creates regulatory and reputational risk. Charter a council, publish review criteria, gate production AI.
Invest in Real-Time Data Pipelines and Event Streams. Data Pipelines (1) and Event-Driven (0) are the gap between data platform maturity and real-time AI. Extend Kafka or Azure Event Hubs to critical flows first.

Methodology

Scores derive from the density and diversity of signals detected across Services, Tools, Concepts, and Standards, aggregated into 11 strategic layers mapping the full technology stack.

Based on signal data available as of April 2026.

Capabilities are Naftiko definitions for how enterprise services, tools, and standards can be combined to deliver business value using AI. Each capability below defines the integrations, workflows, and orchestrations available — run them with the Naftiko Framework or browse the shipped set in the Naftiko Fleet.

Sort

Expand

33 capabilities

Conduct Quarterly Technology Posture Review ▼

enterprise automation

Conduct Quarterly Technology Posture Review — contextualized for LVMH based on its actual service and tool footprint.

naftiko: '0.5'
info:
  label: Conduct Quarterly Technology Posture Review
  description: Conduct Quarterly Technology Posture Review — contextualized for LVMH based on its actual service and tool footprint.
  tags:
  - enterprise
  - automation
capability:
  exposes:
  - type: mcp
    namespace: posture-review
    port: 8080
    tools:
    - name: execute
      description: Conduct Quarterly Technology Posture Review — contextualized for LVMH based on its actual service and tool footprint.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: posture-review.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: posture-review
    baseUri: https://lvmh.internal.example.com/posture-review/v1
    authentication:
      type: bearer
      token: $secrets.posture_review_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → conduct-quarterly-technology-posture-review.yml

Email Phishing Training Campaign Dispatch ▼

enterprise automation

Email Phishing Training Campaign Dispatch — contextualized for LVMH based on its actual service and tool footprint.

naftiko: '0.5'
info:
  label: Email Phishing Training Campaign Dispatch
  description: Email Phishing Training Campaign Dispatch — contextualized for LVMH based on its actual service and tool footprint.
  tags:
  - enterprise
  - automation
capability:
  exposes:
  - type: mcp
    namespace: phish-train
    port: 8080
    tools:
    - name: execute
      description: Email Phishing Training Campaign Dispatch — contextualized for LVMH based on its actual service and tool footprint.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: phish-train.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: phish-train
    baseUri: https://lvmh.internal.example.com/phish-train/v1
    authentication:
      type: bearer
      token: $secrets.phish_train_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → email-phishing-training-campaign-dispatch.yml

Enterprise Glossary Term Suggestions ▼

enterprise automation

Enterprise Glossary Term Suggestions — contextualized for LVMH based on its actual service and tool footprint.

naftiko: '0.5'
info:
  label: Enterprise Glossary Term Suggestions
  description: Enterprise Glossary Term Suggestions — contextualized for LVMH based on its actual service and tool footprint.
  tags:
  - enterprise
  - automation
capability:
  exposes:
  - type: mcp
    namespace: glossary-ai
    port: 8080
    tools:
    - name: execute
      description: Enterprise Glossary Term Suggestions — contextualized for LVMH based on its actual service and tool footprint.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: glossary-ai.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: glossary-ai
    baseUri: https://lvmh.internal.example.com/glossary-ai/v1
    authentication:
      type: bearer
      token: $secrets.glossary_ai_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → enterprise-glossary-term-suggestions.yml

Generate Weekly Executive Technology Digest ▼

enterprise automation

Generate Weekly Executive Technology Digest — contextualized for LVMH based on its actual service and tool footprint.

naftiko: '0.5'
info:
  label: Generate Weekly Executive Technology Digest
  description: Generate Weekly Executive Technology Digest — contextualized for LVMH based on its actual service and tool footprint.
  tags:
  - enterprise
  - automation
capability:
  exposes:
  - type: mcp
    namespace: exec-digest
    port: 8080
    tools:
    - name: execute
      description: Generate Weekly Executive Technology Digest — contextualized for LVMH based on its actual service and tool footprint.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: exec-digest.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: exec-digest
    baseUri: https://lvmh.internal.example.com/exec-digest/v1
    authentication:
      type: bearer
      token: $secrets.exec_digest_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → generate-weekly-executive-technology-digest.yml

Risk Register Nightly Sync ▼

enterprise automation

Risk Register Nightly Sync — contextualized for LVMH based on its actual service and tool footprint.

naftiko: '0.5'
info:
  label: Risk Register Nightly Sync
  description: Risk Register Nightly Sync — contextualized for LVMH based on its actual service and tool footprint.
  tags:
  - enterprise
  - automation
capability:
  exposes:
  - type: mcp
    namespace: risk-register
    port: 8080
    tools:
    - name: execute
      description: Risk Register Nightly Sync — contextualized for LVMH based on its actual service and tool footprint.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: risk-register.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: risk-register
    baseUri: https://lvmh.internal.example.com/risk-register/v1
    authentication:
      type: bearer
      token: $secrets.risk_register_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → risk-register-nightly-sync.yml

Software Asset Reclaim from Idle Licenses ▼

enterprise automation

Software Asset Reclaim from Idle Licenses — contextualized for LVMH based on its actual service and tool footprint.

naftiko: '0.5'
info:
  label: Software Asset Reclaim from Idle Licenses
  description: Software Asset Reclaim from Idle Licenses — contextualized for LVMH based on its actual service and tool footprint.
  tags:
  - enterprise
  - automation
capability:
  exposes:
  - type: mcp
    namespace: asset-reclaim
    port: 8080
    tools:
    - name: execute
      description: Software Asset Reclaim from Idle Licenses — contextualized for LVMH based on its actual service and tool footprint.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: asset-reclaim.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: asset-reclaim
    baseUri: https://lvmh.internal.example.com/asset-reclaim/v1
    authentication:
      type: bearer
      token: $secrets.asset_reclaim_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → software-asset-reclaim-from-idle-licenses.yml

LVMH API Source Capability ▼

naftiko source-capability api role:api-developer

Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.

naftiko: '0.5'
naftiko_role_recommendation: api-developer
info:
  label: LVMH API Source Capability
  description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
  tags:
  - naftiko
  - source-capability
  - api
  - role:api-developer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-api
    port: 8080
    tools:
    - name: execute
      description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-api.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-api
    baseUri: https://api.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_api_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-api-developer.yml

LVMH API Source Capability ▼

naftiko source-capability api role:backend-developer

Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.

naftiko: '0.5'
naftiko_role_recommendation: backend-developer
info:
  label: LVMH API Source Capability
  description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
  tags:
  - naftiko
  - source-capability
  - api
  - role:backend-developer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-api
    port: 8080
    tools:
    - name: execute
      description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-api.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-api
    baseUri: https://api.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_api_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-backend-developer.yml

LVMH Data Pipeline Capability ▼

naftiko applied-capability data role:chief-data-officer

Naftiko applied capability that orchestrates LVMH's data sources into a single MCP-ready pipeline for analytics and AI workloads.

naftiko: '0.5'
naftiko_role_recommendation: chief-data-officer
info:
  label: LVMH Data Pipeline Capability
  description: Naftiko applied capability that orchestrates LVMH's data sources into a single MCP-ready pipeline for analytics and AI workloads.
  tags:
  - naftiko
  - applied-capability
  - data
  - role:chief-data-officer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-data
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability that orchestrates LVMH's data sources into a single MCP-ready pipeline for analytics and AI workloads.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-data.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-data
    baseUri: https://data.lvmh.example.com/pipelines/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_data_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-chief-data-officer.yml

LVMH Governance Capability ▼

naftiko applied-capability governance role:chief-information-officer

Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.

naftiko: '0.5'
naftiko_role_recommendation: chief-information-officer
info:
  label: LVMH Governance Capability
  description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
  tags:
  - naftiko
  - applied-capability
  - governance
  - role:chief-information-officer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-governance
    port: 8080
    tools:
    - name: execute
      description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-governance.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-governance
    baseUri: https://governance.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_governance_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-chief-information-officer.yml

LVMH Governance Capability ▼

naftiko applied-capability governance role:chief-information-security-officer

Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.

naftiko: '0.5'
naftiko_role_recommendation: chief-information-security-officer
info:
  label: LVMH Governance Capability
  description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
  tags:
  - naftiko
  - applied-capability
  - governance
  - role:chief-information-security-officer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-governance
    port: 8080
    tools:
    - name: execute
      description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-governance.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-governance
    baseUri: https://governance.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_governance_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-chief-information-security-officer.yml

LVMH Operations Capability ▼

naftiko applied-capability observability role:cloud-architect

Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.

naftiko: '0.5'
naftiko_role_recommendation: cloud-architect
info:
  label: LVMH Operations Capability
  description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
  tags:
  - naftiko
  - applied-capability
  - observability
  - role:cloud-architect
capability:
  exposes:
  - type: mcp
    namespace: lvmh-ops
    port: 8080
    tools:
    - name: execute
      description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-ops.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-ops
    baseUri: https://ops.lvmh.example.com/fabric/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_ops_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-cloud-architect.yml

LVMH Operations Capability ▼

naftiko applied-capability observability role:cloud-engineer

Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.

naftiko: '0.5'
naftiko_role_recommendation: cloud-engineer
info:
  label: LVMH Operations Capability
  description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
  tags:
  - naftiko
  - applied-capability
  - observability
  - role:cloud-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-ops
    port: 8080
    tools:
    - name: execute
      description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-ops.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-ops
    baseUri: https://ops.lvmh.example.com/fabric/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_ops_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-cloud-engineer.yml

LVMH Governance Capability ▼

naftiko applied-capability governance role:cloud-security-engineer

Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.

naftiko: '0.5'
naftiko_role_recommendation: cloud-security-engineer
info:
  label: LVMH Governance Capability
  description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
  tags:
  - naftiko
  - applied-capability
  - governance
  - role:cloud-security-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-governance
    port: 8080
    tools:
    - name: execute
      description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-governance.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-governance
    baseUri: https://governance.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_governance_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-cloud-security-engineer.yml

LVMH Data Pipeline Capability ▼

naftiko applied-capability data role:data-engineer

Naftiko applied capability that orchestrates LVMH's data sources into a single MCP-ready pipeline for analytics and AI workloads.

naftiko: '0.5'
naftiko_role_recommendation: data-engineer
info:
  label: LVMH Data Pipeline Capability
  description: Naftiko applied capability that orchestrates LVMH's data sources into a single MCP-ready pipeline for analytics and AI workloads.
  tags:
  - naftiko
  - applied-capability
  - data
  - role:data-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-data
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability that orchestrates LVMH's data sources into a single MCP-ready pipeline for analytics and AI workloads.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-data.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-data
    baseUri: https://data.lvmh.example.com/pipelines/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_data_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-data-engineer.yml

LVMH Operations Capability ▼

naftiko applied-capability observability role:devops-manager

Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.

naftiko: '0.5'
naftiko_role_recommendation: devops-manager
info:
  label: LVMH Operations Capability
  description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
  tags:
  - naftiko
  - applied-capability
  - observability
  - role:devops-manager
capability:
  exposes:
  - type: mcp
    namespace: lvmh-ops
    port: 8080
    tools:
    - name: execute
      description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-ops.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-ops
    baseUri: https://ops.lvmh.example.com/fabric/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_ops_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-devops-manager.yml

LVMH Operations Capability ▼

naftiko applied-capability observability role:director-of-cloud-engineering

Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.

naftiko: '0.5'
naftiko_role_recommendation: director-of-cloud-engineering
info:
  label: LVMH Operations Capability
  description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
  tags:
  - naftiko
  - applied-capability
  - observability
  - role:director-of-cloud-engineering
capability:
  exposes:
  - type: mcp
    namespace: lvmh-ops
    port: 8080
    tools:
    - name: execute
      description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-ops.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-ops
    baseUri: https://ops.lvmh.example.com/fabric/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_ops_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-director-of-cloud-engineering.yml

LVMH Platform Catalog Capability ▼

naftiko source-capability platform role:director-of-engineering

Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.

naftiko: '0.5'
naftiko_role_recommendation: director-of-engineering
info:
  label: LVMH Platform Catalog Capability
  description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
  tags:
  - naftiko
  - source-capability
  - platform
  - role:director-of-engineering
capability:
  exposes:
  - type: mcp
    namespace: lvmh-platform
    port: 8080
    tools:
    - name: execute
      description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-platform.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-platform
    baseUri: https://backstage.lvmh.example.com/api/catalog
    authentication:
      type: bearer
      token: $secrets.lvmh_platform_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-director-of-engineering.yml

LVMH Platform Catalog Capability ▼

naftiko source-capability platform role:director-of-platform-engineering

Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.

naftiko: '0.5'
naftiko_role_recommendation: director-of-platform-engineering
info:
  label: LVMH Platform Catalog Capability
  description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
  tags:
  - naftiko
  - source-capability
  - platform
  - role:director-of-platform-engineering
capability:
  exposes:
  - type: mcp
    namespace: lvmh-platform
    port: 8080
    tools:
    - name: execute
      description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-platform.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-platform
    baseUri: https://backstage.lvmh.example.com/api/catalog
    authentication:
      type: bearer
      token: $secrets.lvmh_platform_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-director-of-platform-engineering.yml

LVMH Platform Catalog Capability ▼

naftiko source-capability platform role:engineering-manager

Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.

naftiko: '0.5'
naftiko_role_recommendation: engineering-manager
info:
  label: LVMH Platform Catalog Capability
  description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
  tags:
  - naftiko
  - source-capability
  - platform
  - role:engineering-manager
capability:
  exposes:
  - type: mcp
    namespace: lvmh-platform
    port: 8080
    tools:
    - name: execute
      description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-platform.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-platform
    baseUri: https://backstage.lvmh.example.com/api/catalog
    authentication:
      type: bearer
      token: $secrets.lvmh_platform_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-engineering-manager.yml

LVMH API Source Capability ▼

naftiko source-capability api role:full-stack-developer

Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.

naftiko: '0.5'
naftiko_role_recommendation: full-stack-developer
info:
  label: LVMH API Source Capability
  description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
  tags:
  - naftiko
  - source-capability
  - api
  - role:full-stack-developer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-api
    port: 8080
    tools:
    - name: execute
      description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-api.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-api
    baseUri: https://api.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_api_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-full-stack-developer.yml

LVMH API Source Capability ▼

naftiko source-capability api role:head-of-api

Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.

naftiko: '0.5'
naftiko_role_recommendation: head-of-api
info:
  label: LVMH API Source Capability
  description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
  tags:
  - naftiko
  - source-capability
  - api
  - role:head-of-api
capability:
  exposes:
  - type: mcp
    namespace: lvmh-api
    port: 8080
    tools:
    - name: execute
      description: Naftiko source capability wrapping LVMH's primary API surface, exposing it via MCP and REST for downstream reuse.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-api.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-api
    baseUri: https://api.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_api_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-head-of-api.yml

LVMH MCP Context Tool ▼

naftiko applied-capability mcp role:head-of-integration

Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.

naftiko: '0.5'
naftiko_role_recommendation: head-of-integration
info:
  label: LVMH MCP Context Tool
  description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
  tags:
  - naftiko
  - applied-capability
  - mcp
  - role:head-of-integration
capability:
  exposes:
  - type: mcp
    namespace: lvmh-context
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-context.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-context
    baseUri: https://internal.lvmh.example.com/context/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_context_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-head-of-integration.yml

LVMH Platform Catalog Capability ▼

naftiko source-capability platform role:head-of-platform-engineering

Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.

naftiko: '0.5'
naftiko_role_recommendation: head-of-platform-engineering
info:
  label: LVMH Platform Catalog Capability
  description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
  tags:
  - naftiko
  - source-capability
  - platform
  - role:head-of-platform-engineering
capability:
  exposes:
  - type: mcp
    namespace: lvmh-platform
    port: 8080
    tools:
    - name: execute
      description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-platform.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-platform
    baseUri: https://backstage.lvmh.example.com/api/catalog
    authentication:
      type: bearer
      token: $secrets.lvmh_platform_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-head-of-platform-engineering.yml

LVMH MCP Context Tool ▼

naftiko applied-capability mcp role:integration-architect

Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.

naftiko: '0.5'
naftiko_role_recommendation: integration-architect
info:
  label: LVMH MCP Context Tool
  description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
  tags:
  - naftiko
  - applied-capability
  - mcp
  - role:integration-architect
capability:
  exposes:
  - type: mcp
    namespace: lvmh-context
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-context.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-context
    baseUri: https://internal.lvmh.example.com/context/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_context_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-integration-architect.yml

LVMH MCP Context Tool ▼

naftiko applied-capability mcp role:mlops-engineer

Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.

naftiko: '0.5'
naftiko_role_recommendation: mlops-engineer
info:
  label: LVMH MCP Context Tool
  description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
  tags:
  - naftiko
  - applied-capability
  - mcp
  - role:mlops-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-context
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-context.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-context
    baseUri: https://internal.lvmh.example.com/context/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_context_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-mlops-engineer.yml

LVMH Platform Catalog Capability ▼

naftiko source-capability platform role:platform-engineer

Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.

naftiko: '0.5'
naftiko_role_recommendation: platform-engineer
info:
  label: LVMH Platform Catalog Capability
  description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
  tags:
  - naftiko
  - source-capability
  - platform
  - role:platform-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-platform
    port: 8080
    tools:
    - name: execute
      description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-platform.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-platform
    baseUri: https://backstage.lvmh.example.com/api/catalog
    authentication:
      type: bearer
      token: $secrets.lvmh_platform_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-platform-engineer.yml

LVMH MCP Context Tool ▼

naftiko applied-capability mcp role:prompt-engineer

Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.

naftiko: '0.5'
naftiko_role_recommendation: prompt-engineer
info:
  label: LVMH MCP Context Tool
  description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
  tags:
  - naftiko
  - applied-capability
  - mcp
  - role:prompt-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-context
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-context.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-context
    baseUri: https://internal.lvmh.example.com/context/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_context_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-prompt-engineer.yml

LVMH Governance Capability ▼

naftiko applied-capability governance role:security-engineer

Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.

naftiko: '0.5'
naftiko_role_recommendation: security-engineer
info:
  label: LVMH Governance Capability
  description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
  tags:
  - naftiko
  - applied-capability
  - governance
  - role:security-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-governance
    port: 8080
    tools:
    - name: execute
      description: Applied capability exposing governance metadata, policy compliance, and audit trails for LVMH's capability fabric.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-governance.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-governance
    baseUri: https://governance.lvmh.example.com/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_governance_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-security-engineer.yml

LVMH Operations Capability ▼

naftiko applied-capability observability role:site-reliability-engineer

Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.

naftiko: '0.5'
naftiko_role_recommendation: site-reliability-engineer
info:
  label: LVMH Operations Capability
  description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
  tags:
  - naftiko
  - applied-capability
  - observability
  - role:site-reliability-engineer
capability:
  exposes:
  - type: mcp
    namespace: lvmh-ops
    port: 8080
    tools:
    - name: execute
      description: Applied capability surfacing fabric-wide health, latency, and circuit-breaker state for LVMH's SREs and operators.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-ops.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-ops
    baseUri: https://ops.lvmh.example.com/fabric/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_ops_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-site-reliability-engineer.yml

LVMH MCP Context Tool ▼

naftiko applied-capability mcp role:solutions-architect

Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.

naftiko: '0.5'
naftiko_role_recommendation: solutions-architect
info:
  label: LVMH MCP Context Tool
  description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
  tags:
  - naftiko
  - applied-capability
  - mcp
  - role:solutions-architect
capability:
  exposes:
  - type: mcp
    namespace: lvmh-context
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-context.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-context
    baseUri: https://internal.lvmh.example.com/context/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_context_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-solutions-architect.yml

LVMH MCP Context Tool ▼

naftiko applied-capability mcp role:technical-lead

Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.

naftiko: '0.5'
naftiko_role_recommendation: technical-lead
info:
  label: LVMH MCP Context Tool
  description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
  tags:
  - naftiko
  - applied-capability
  - mcp
  - role:technical-lead
capability:
  exposes:
  - type: mcp
    namespace: lvmh-context
    port: 8080
    tools:
    - name: execute
      description: Naftiko applied capability composed over LVMH's existing source APIs, exposed as an MCP tool for agent context engineering.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-context.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-context
    baseUri: https://internal.lvmh.example.com/context/v1
    authentication:
      type: bearer
      token: $secrets.lvmh_context_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-technical-lead.yml

LVMH Platform Catalog Capability ▼

naftiko source-capability platform role:vp-of-engineering

Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.

naftiko: '0.5'
naftiko_role_recommendation: vp-of-engineering
info:
  label: LVMH Platform Catalog Capability
  description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
  tags:
  - naftiko
  - source-capability
  - platform
  - role:vp-of-engineering
capability:
  exposes:
  - type: mcp
    namespace: lvmh-platform
    port: 8080
    tools:
    - name: execute
      description: Source capability that publishes LVMH's Naftiko fabric into Backstage for catalog, scaffolding, and discovery.
      inputParameters:
      - name: context
        in: body
        type: string
        description: Contextual parameter for the workflow.
      call: lvmh-platform.execute
      with:
        context: '{{context}}'
  consumes:
  - type: http
    namespace: lvmh-platform
    baseUri: https://backstage.lvmh.example.com/api/catalog
    authentication:
      type: bearer
      token: $secrets.lvmh_platform_token
    resources:
    - name: default
      path: /actions/execute
      inputParameters: []
      operations:
      - name: execute
        method: POST

Open in Framework → View in Fleet → whats-next-vp-of-engineering.yml

LVMH AI Enterprise Navigation

An analysis of LVMH's enterprise technology investments surfaces 50 recommendations across 10 strategic groups, grounded in a total signal score of 0. The featured top 5 address the highest-leverage gaps first; the remaining 45 are organized by theme for deeper exploration and quarterly review. Strongest current area: core technology (0).

Top Priorities — Featured

Establish an API-First Integration Strategy

High

AI Foundation

With an API score of 0 against 0 services in use, LVMH lacks the connective tissue to expose AI capabilities consistently. Prioritize API standards adoption, deploy an API gateway, and publish a service catalog so agents and internal teams can invoke AI services reliably across business units.

Invest in Real-Time Data Pipeline Infrastructure

High

Data & Knowledge

Data pipelines score of 0 against Data score of 0 indicates LVMH has data at rest but can't stream it. Invest in Kafka, Flink, or managed equivalents to enable real-time AI inference and event-driven workflows.

Deploy a Model Registry with Version Control

High

Customization

Model registry score of 0 means LVMH can't reliably answer 'which model is in production.' Deploy MLflow, SageMaker Model Registry, or Azure ML model registry, enforce signed checkpoints, and wire approval workflows into every deployment.

Standardize an Event Backbone with Kafka or EventBridge

High

Integration

Event-driven score of 0 leaves LVMH batch-bound. Stand up Kafka, Kinesis, or EventBridge as the enterprise event backbone and re-platform the three most business-critical integrations onto it first.

Establish an AI Governance Council

High

Governance & Risk

Governance score of 0 plus accelerating AI adoption calls for a named AI governance council at LVMH with quarterly reviews, explicit approval authority, and a published decision log visible to line businesses.

Further Opportunities — 48 more

Expand

AI Foundation

4 ▼

Formalize LLM Platform Selection and Access High

LVMH's AI score of 0 indicates early-stage LLM adoption. Pick one or two strategic foundation-model providers (e.g., Anthropic, OpenAI, Azure OpenAI, Bedrock), publish rate-limited shared access, and retire ad-hoc team-level accounts to consolidate spend and security review.

Adopt an Open-Source Contribution Policy High

Open-source score of 0 suggests LVMH consumes significant OSS (via major tools) without a formal contribution or compliance policy. Publish a CONTRIBUTING.md, adopt SBOM generation, and set clear rules for employee contributions to OSS projects.

Define an API Versioning and Deprecation Policy High

With an API score of 0, LVMH likely has APIs that break consumers without notice. Publish a semver-based API versioning policy, a 12-month deprecation window, and an automated gateway check that rejects unannounced breaking changes.

Build an Internal AI Sandbox for Experimentation Medium

Even with an AI score of 0, most teams at LVMH likely experiment informally. Stand up a sandboxed AI environment with pre-approved models, budget limits, and logging so teams can prototype without procurement friction while governance still has visibility.

Data & Knowledge

4 ▼

Publish a Canonical Data Catalog High

Data score of 0 reflects breadth but not discoverability. Deploy a data catalog (Collibra, Alation, Atlan, or open-source DataHub) on top of your data platforms so teams find data without asking.

Adopt Schema Validation at Data Ingress High

Specifications score of 0 suggests LVMH accepts ungoverned data formats. Adopt JSON Schema, Avro, or Protobuf at every data-ingress point and reject non-conforming payloads upstream of the warehouse.

Rationalize Your Database Portfolio High

With 0 database signals, LVMH likely runs too many stores. Map each database to a workload pattern (transactional, analytical, cache, vector, time-series) and retire duplicates to cut license cost and operational toil.

Codify a Data Quality SLO Framework High

LVMH's Data score of 0 isn't matched by obvious data quality governance. Establish SLOs for freshness, completeness, and correctness per critical dataset, wire alerts to the on-call data team, and publish a monthly data-quality scorecard.

Customization

4 ▼

Invest in Multimodal Infrastructure for Image/Audio/Video High

Multimodal score of 0 shows early investment. Build out pipelines for image OCR, audio transcription, and video analysis, centralized so every team uses the same extractors rather than reimplementing per project.

Establish a Domain-Specific Model Library High

Domain specialization score of 0 indicates LVMH relies on general models for domain tasks. Maintain a curated list of 3–5 domain-tuned models (legal, medical, code, finance) with benchmarks so teams pick the right model without trial-and-error.

Pilot Fine-Tuning on a High-Value Vertical Workflow Medium

Before investing in broad fine-tuning infrastructure, pick one vertical workflow at LVMH where domain-specific language matters (e.g., contracts, clinical notes, support transcripts) and fine-tune a base model to measure real lift vs. prompt engineering.

Standardize Prompt Template Governance Medium

Prompts are production code. Version them in Git, review them in PRs, run them through eval harnesses, and forbid checked-in prompts that embed secrets or PII — at LVMH's current scale, prompt drift is the leading cause of silent quality regression.

Efficiency

7 ▼

Extend Automation Coverage to High-Toil Operational Tasks High

Automation score of 0 on ServiceNow and Ansible can be extended by identifying the top 20 manually-repeated operational tickets at LVMH and implementing ticket-triggered automations for each.

Adopt Kubernetes for All Stateless Workloads High

Containers score of 0 suggests partial adoption. Commit to Kubernetes (managed: EKS, AKS, or GKE) as the default runtime for every new stateless service at LVMH, and fold legacy VM workloads into the cluster on a 12-month plan.

Build an Internal Developer Platform High

With a Platform score of 0, LVMH likely has each team rolling its own scaffolds. Invest in a Backstage-based internal developer portal that offers golden-path templates for service creation, observability, and compliance out of the box.

Define SRE Ownership for Every Production Service High

Operations score of 0 across ServiceNow and Datadog needs SRE discipline. Assign a named SRE team (or primary engineer) to every Tier-1 service and hold quarterly reliability reviews.

Adopt Infrastructure as Code Across All Environments Medium

Wherever Terraform, Ansible, or CloudFormation appears in LVMH's tools, ensure 100% coverage. Manual cloud changes in production should fail a drift-detection job and block deployment; this tightens security posture and makes disaster recovery tractable.

Invest in an Internal Developer Platform High

Stand up a Backstage IDP at LVMH with Naftiko's plugin so Cloud Architects offer golden-path capability templates instead of one-off scaffolds.

Invest in an Internal Developer Platform High

Stand up a Backstage IDP at LVMH with Naftiko's plugin so Engineering Managers offer golden-path capability templates instead of one-off scaffolds.

Productivity

5 ▼

Rationalize SaaS Portfolio and Cut Duplicate Subscriptions High

With an estimated SaaS signal of 0 and 0 services detected, LVMH likely has overlapping subscriptions (CRM, support, project management). Run a quarterly SaaS rationalization review backed by usage analytics from Productiv, Vantage, or similar.

Deploy Single Sign-On Across All SaaS Applications High

Every SaaS application at LVMH should authenticate through a central IdP (Okta, Entra ID, or similar). SSO coverage is a security control, a compliance artifact, and an employee-productivity win — yet is nearly always incomplete at scale.

Reduce Programming-Language Proliferation High

With 0 programming languages detected in LVMH's hiring signals, maintenance cost is real. Declare 3–5 primary languages officially supported for new services and require justification for others.

Invest in Knowledge-Base Search That Is LLM-Ready Medium

SharePoint, Confluence, and Google Drive are core, but most LVMH employees still can't find what they need. Instrument search success rates and fold the corpus into the LLM retrieval stack so agents can answer 'where's the …' with confidence.

Consolidate Communication Platforms Medium

If LVMH has Microsoft Teams, Slack, WebEx, and others in parallel, the split discourages cross-org knowledge flow. Pick one primary tool and set the others to sunset in a 6-month window with clear migration paths.

Integration

4 ▼

Adopt an iPaaS for Cross-System Orchestration High

Integrations score of 0 and core enterprise platforms calls for a formal iPaaS (Boomi, MuleSoft, Workato, or Oracle Integration) rather than ad-hoc point-to-point integrations.

Pilot MCP Servers for Internal System Access Medium

Model Context Protocol lets AI agents call internal systems safely. Pick 3 high-value systems at LVMH (ticketing, HR lookup, billing) and stand up MCP servers behind approval workflows so agents can act with audit trails.

Introduce a Service Mesh for East-West Traffic Security High

Patterns score of 0 and Kubernetes presence indicate LVMH is ready for Istio, Linkerd, or Consul service-mesh. Get mTLS, retries, and rate-limiting at the infrastructure layer instead of re-implementing them in every service.

Strengthen Cloud-Native Adoption with CNCF Projects High

CNCF score of 0 can be broadened by bringing in OpenTelemetry for observability, Envoy for L7 proxying, and Argo for workflow orchestration alongside the Kubernetes investments LVMH already has.

Measurement

5 ▼

Consolidate Observability onto a Single Vendor High

Observability score of 0 across multiple APMs suggests overlap. Pick one primary observability provider, sunset the rest within 12 months, and reinvest savings into better alerting.

Ship a Testing & Quality Benchmark Suite for LLM Features High

Testing score of 0 doesn't cover generative AI quality today. Define evaluation suites (accuracy, hallucination rate, latency) for every LLM feature at LVMH and block releases that regress.

Tie AI Initiatives to ROI and Business Metrics High

ROI score of 0 needs AI-specific measurement. For every AI feature at LVMH, publish a scorecard showing dollars saved, hours saved, or revenue influenced per quarter — otherwise budget pressure will cut programs that work.

Establish a Developer Productivity Metric Program High

Developer experience score of 0 should be tracked via DORA metrics — deployment frequency, lead time, change failure rate, MTTR — shared in a living dashboard that executives and teams both see.

Adopt OpenTelemetry as the Shared Instrumentation Layer Medium

Regardless of vendor, instrument every LVMH service with OpenTelemetry SDKs. This decouples instrumentation from vendor choice and means the next renewal negotiation isn't a migration project.

Governance & Risk

4 ▼

Formalize AI Review & Approval Workflows High

AI review score of 0 means LVMH lacks a repeatable process for vetting new AI use cases. Publish a lightweight risk intake form (data sensitivity, decision consequence, model type), tier responses by risk, and route accordingly.

Strengthen Security Posture for AI-Specific Threats High

Security score of 0 on core perimeter tools must now cover prompt injection, model theft, and training-data poisoning. Add an AI-specific red-team exercise to the annual security plan.

Map AI Controls to Existing Regulatory Frameworks High

Regulatory score of 0 shows LVMH can't easily answer 'are we compliant with the AI Act / NIST AI RMF?' Map existing SOC 2, ISO, GDPR, and HIPAA controls to AI-specific asks so compliance audits are additive, not a rebuild.

Implement Data Privacy Controls for AI Training Inputs High

Privacy score of 0 is too low to train on customer data safely. Establish an 'is this training-eligible?' data classification flag that every LLM fine-tuning job at LVMH must honor before data hits a pipeline.

Economics

5 ▼

Stand Up an AI FinOps Practice High

FinOps score of 0 against growing cloud and model spend at LVMH means AI costs will surprise before they optimize. Dedicate a FinOps engineer to model/inference costs with monthly reporting and hard team budgets.

Formalize a Provider/Vendor Strategy High

Provider strategy score of 0 indicates vendor sprawl. Name a primary cloud, primary identity provider, primary observability vendor, and primary LLM provider at LVMH — and require an exception process (with a sunset date) for anything else.

Diversify Strategic Partnerships Beyond Hyperscalers High

Partnerships score of 0 suggests heavy hyperscaler dependency. Cultivate deeper relationships with 2–3 specialized AI partners (data labeling, vector infra, model eval) so LVMH isn't hostage to one provider's roadmap.

Adopt Tagging for All Cloud and AI Resources Medium

Without consistent cost allocation tags (cost-center, team, product, env, ai-workload), FinOps at LVMH is guesswork. Make tag-absence a deployment-blocking policy in CI.

Negotiate Committed-Spend Contracts Once Usage Is Predictable Medium

After 6 months of stable AI/cloud spend, renegotiate for committed-use discounts (CUDs on GCP, Savings Plans on AWS, Reserved Instances on Azure) — typical savings of 30–60% at LVMH's scale easily funds a year of governance work.

Strategic

5 ▼

Publish an Executive AI Strategy Aligned to Business Outcomes High

Alignment score of 0 needs a written AI strategy anchored to 3–5 measurable business outcomes at LVMH — not tech-for-tech's-sake programs. Refresh annually with the signals data underneath this navigation.

Build an Internal AI Talent Development Program High

Talent score of 0 reflects hiring signal but not retention infrastructure. Fund an AI skill academy with hands-on tracks for engineers, analysts, and product managers — cheaper and stickier than external hiring for key skills.

Adopt Industry Standards as a Differentiator High

Standardization score of 0 indicates LVMH can leapfrog peers by committing to OpenAPI, AsyncAPI, JSON Schema, SBOM, and OpenTelemetry as enterprise defaults — each carries compounding integration benefits.

Add AI Due Diligence to M&A Playbooks High

M&A score of 0 suggests activity. Every future LVMH acquisition should include an AI-posture due diligence checklist (model inventory, training data provenance, AI vendor contracts, incident history) as a hard gate.

Reorganize Around Outcomes, Not Technology Silos Medium

Organize AI teams around customer or employee outcomes (acquisition, retention, service quality) rather than technology verticals (ML, data, platform). This pushes AI toward shipped value rather than internal-consumption infrastructure.

Score Breakdown — Total: 20

Each row links to its full definition on the Naftiko Signal Groups page.