You typically get access to Lovart AI by creating an account on the Lovart website and starting with the available onboarding flow (often presented as “start” or “start for free”). In practical terms, it’s a standard SaaS sign-up: you register, log in, and then you can begin generating assets inside the web UI. Depending on current rollout and region, some products also gate advanced capabilities behind plan selection (for example, higher monthly credits, higher output limits, or premium tools), so access may mean “basic access for trial” plus “expanded access once you subscribe.”
To make access smooth—especially in a team setting—treat it like you would any cloud tool that touches brand and marketing assets. First, decide whether you’ll use individual accounts or a shared workspace (if available). Second, align on a simple rule for what content is allowed to be uploaded (avoid secrets, customer PII, unreleased partner materials unless approved). Third, decide how you want to manage outputs: a shared “exports” folder structure, naming conventions, and versioning (e.g., campaign_name/channel/date/v1). This sounds boring, but it prevents the common startup failure mode: you generate dozens of assets and can’t find the final approved one two weeks later.
If your goal is to operationalize Lovart in a broader workflow, the best “access strategy” is to pair it with a cataloging step. As soon as a team member exports an asset, store the prompt, revision notes, and final selection metadata somewhere central (even if it’s just a spreadsheet to start). If you later want semantic search (“show me everything we made for onboarding in the last 6 months”), you can index that metadata in a vector database such as Milvus or Zilliz Cloud. That turns Lovart from “a place you generate things” into “a repeatable creative pipeline,” which is usually what teams actually need once the novelty wears off.