Artiplane · fashion & luxury

Artiplane frequently asked questions

Platform, integrations, AI models, data security, roles and costs: short, verifiable answers.

  • What is Artiplane in one sentence?

    Artiplane is the AI operating layer for fashion and luxury: it connects enterprise systems, unifies them in a fashion semantic model, runs governed agents and Digital Workers on top, and delivers the result in a role-based Worker App.

  • How is it different from BI or a dashboard?

    BI shows the past. Artiplane proposes the action, simulates it, routes it to whoever has authority and measures the outcome after execution.

  • What are the layers of the architecture?

    Six: Systems, Users, AI layer, Semantic layer, Digital Worker and Digital App (the Worker Apps).

  • What is the difference between an agent and a Digital Worker?

    An agent performs a task across one or more systems. A Digital Worker is the alter ego of a role: it coordinates several agents and workflows, proposes activities and stays subject to human approval.

  • What is a Worker App?

    It is the operational application of a role: buying, pricing, planning, supply chain, clienteling, customer service, factory. It is built and renamed in the Control Plane and opens full screen.

  • Which systems can be connected?

    SAP, PLM, PIM, CRM, POS, WMS, commerce, data platforms and automation tools such as n8n: the demo landscape counts more than 35 applications.

  • Does Artiplane write directly into the systems?

    No. Every write is a registered action with its target system, input contract, approval threshold and execution record. Agents can request one; they cannot invent one.

  • How are fields aligned across different systems?

    With a field-by-field mapping onto the canonical model, versioned through draft, test and publish, always with visible lineage.

  • Does it also use public industry data?

    Yes: weather by market, competitor assortments and price moves, trend signals, all mapped onto the company's semantic categories.

  • Which AI models can be used?

    Anthropic Claude, OpenAI, Google Gemini, native runtimes and n8n or SAP BTP workflows. The model is chosen per agent and can be switched off or replaced from the AI Hub.

  • How does RAG on company documents work?

    Each RAG base is a governed object with sources, refresh, per-area permissions and measured cost: answers always cite the documents used.

  • How are made-up answers prevented?

    Numbers come from the semantic layer, not from the model; the model explains and proposes. Guardrails, control steps and human approval close the loop.

  • Is AI spending visible?

    Yes: the AI Hub shows last-30-days cost, requests and tokens per model, agent, RAG and runtime, with drill-down to a single agent.

  • Is it clear where Claude or another model intervenes?

    Yes: every reasoning step names the model that runs it, and agents imported from market LLMs always show their provenance.

  • How is log integrity guaranteed?

    Every logged interaction carries an SHA-256 compliance hash; content storage is opt-in and retention is configurable.

  • How do you approach the EU AI Act?

    With risk classes per use case, mandatory human oversight where required, documented model provenance and configuration audit.

  • Does data stay in Europe?

    Isolation level is a configuration choice: standard, EU-hosted only or on-premise, with the models allowed for each level.

  • Does the demo use real data?

    No. Actions shown in the public experience are not sent to enterprise systems. Access is provided to explore behaviours, controls and operational flows.

  • How many types of access are there?

    Two: Admin, which configures the platform, and User, which operates. Admin does not approve business decisions; the user does not change configuration.

  • How is what a user sees limited?

    Per Worker App you define authorized users with restrictions: geographies, product categories, read-only or write, budget and operating window.

  • How do activities reach people?

    The Digital Worker proposes them or the user creates them; they can be one-off or recurring, they carry dependencies and close with an outcome, attachments and a downloadable output.

  • How do changes reach production?

    Like in SAP: each change becomes a logged Change Request, with environment and version visible, transported to production in one click.

  • How long does the first use case take?

    You start from one process and the systems feeding it: connection, mapping, one Digital Worker and its Worker App are configuration work, not development.

  • Do you need a data science team?

    No. You need people who know the processes: the Control Plane is designed for IT and function owners, with no code to write.

  • How do I access the demos?

    From the access page, with the code we provide: you enter as Admin or as User, in the language you prefer.

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