MNML

MNML is a drawing to image tool. An AI rendering and visualization studio for architects, turning sketches, photos and SketchUp, Revit or Rhino views into editable, upscalable renders, with a comparison board, free modeling plugins and credit-based pricing.

MNML is an AI rendering studio aimed squarely at architects and the people who work alongside them: visualization teams, competition crews, interior designers and landscape planners. The premise is narrow on purpose. Instead of a general image generator that needs a paragraph of prompt engineering, MNML takes what you already have (a sketch, a site photo, or a view exported from SketchUp, Revit, Rhino or Blender) and turns it into a photoreal image without lighting, material or render engine setup. The company reports 2.5 million registered users and use at more than 2,400 firms and 250 universities. The product splits into two workspaces that share one project, library and credit balance. Studio follows a single image through the whole arc: a guided render where light, season, materials and camera are controls rather than prompt text, then region-level edits with a mask, object removal, frame extension and upscaling to 8K, and finally an optional short clip with shots named the way an architect would brief them (walkthrough, drone view, hero shot). Flow is an infinite board where up to four variations land next to their source, each carrying a record of the references and prompt behind it, so options get compared rather than guessed at. Collaborators can join a board as viewers or editors, with sticky notes and live cursors for review sessions. Beyond the browser, free plugins put a MNML panel inside SketchUp 2026, Revit 2024 to 2027, Rhino 8 and Grasshopper, rendering the active viewport and placing the result back into the model in one undo. A connector also brings rendering and editing into ChatGPT, Claude and other assistants. Pricing runs on credits, with the cost shown on the button before any spend, and new accounts start with free credits and no card. Teams get pooled credits, per-member caps and role-based access. Where it fits: it competes with visualization-specific AI renderers and with generic image models, and it wins on architectural vocabulary and file lineage rather than on sheer stylistic range. Studios already deep in a traditional pipeline can use it as a fast concept and iteration layer, while solo architects may find the credit system and project structure heavier than a one-shot tool.