Start with the artifact, not a blank page
ML Clever is organized around the thing you need to create: a document, presentation, dashboard, website, or chat answer.
Keep context close to generation
Prompts can carry files, datasets, templates, themes, page ranges, and mode-specific settings into the workflow.
Move from draft to usable work
The platform creates structured outputs you can open, refine, share, and keep in your library instead of disposable AI text.
The creation modes
Each mode gives the backend the right context for the artifact being generated.
AI documents
Reports, briefs, proposals, memos, and source-backed docs generated with themes and templates.
AI presentations
Deck workflows that move through research, outlines, slide plans, visual style, and editable presentation output.
Static AI dashboards
Dashboard-style web experiences for business reviews, metrics, and custom static reporting surfaces.
AI websites
Landing pages, portfolio pages, market pages, and custom web pages generated from prompts and context.
How the workspace turns context into output
Prompt, choose a mode, attach useful context, and let the generation workflow create a real artifact.
Prompt to document
Docs use themes, templates, page-range controls, and source context to create structured business writing.
01What is ML Clever building?
What is ML Clever building?
ML Clever is an AI workspace for creating business artifacts. The core creation modes are chat, documents, presentations, dashboards, and websites.
02Why combine documents, presentations, dashboards, and websites?
Why combine documents, presentations, dashboards, and websites?
Business work rarely stays in one format. A research prompt might become a document, a deck, a web page, or a dashboard-style page depending on the audience. ML Clever keeps those workflows close together.
03How are dashboards different from BI tools?
How are dashboards different from BI tools?
ML Clever is not trying to replace connected BI platforms. Dashboards are generated as static, customizable web experiences, which makes them useful for polished reporting, investor updates, internal readouts, and client-facing pages.
04What makes the workflow different from a normal chatbot?
What makes the workflow different from a normal chatbot?
Chat is still available, but creation modes pass more structured metadata to the backend: themes, templates, slide counts, page ranges, files, datasets, and generation targets. That lets the system create artifacts instead of only answering in text.