Monet vs Claude: AI Event Floor Plans Compared
What a frontier language model can and cannot do with an event floor plan, and how to check it yourself.
This guide sets out what Claude can and cannot do with an event floor plan, from Anthropic's own help pages, and what Monet is built to do. It turns the question into six checks, from reading the venue file to surviving a late change. You can run them on any language model.
Frontier language models reason well about rules and numbers. So the fair question is what one can and cannot do with an event floor plan. This guide sets a Claude event floor plan beside Monet, using what each tool's documentation says. It turns the question into six checks you can run on your own venue.
A fair question for a language model
Reasoning and geometry are different skills. A model can check that a brief's aisles and exit paths add up. A floor plan also needs every line placed on a real room at true scale. The checks below ask for both and show where the line falls. The same standard of scale, change and export applies to ten AI floor plan generators.
What Claude is built to do
Claude reads uploaded PDFs. For PDFs of up to 100 pages it also reads the charts and images inside (uploading files). It reasons step by step about rules and writes code. It can run code to create files such as spreadsheets and PDFs (file creation). It builds diagrams and visuals in HTML and SVG. Anthropic's help center says it does not generate photos or illustrations the way image tools do (Claude and images).
For planners, that means checking a brief for contradictions and working through exit-width arithmetic. It also means comparing supplier quotes and drafting risk assessments. For those jobs Claude is the better choice. Monet is a layout tool, not a reading and writing assistant.
What Claude can and cannot do with an event floor plan
- Reads the venue PDF. What the documentation says: Reads text and visuals in PDFs up to 100 pages. How to check it: Compare its list of dimensions and exits with ones you measured.
- Computes exit capacity. What the documentation says: Reasons step by step from a rule you give. How to check it: Check its numbers against the rule your authority uses.
- Draws a plan. What the documentation says: Builds diagrams in SVG and HTML. How to check it: Ask for SVG at 1 unit = 1 mm on the hall outline.
- Writes a CAD file. What the documentation says: Runs code to create files. How to check it: Ask for Python that writes a DXF, for example with ezdxf.
- Overlays the venue. What the documentation says: No floor plan feature is described. How to check it: Open the output at full size over the venue DWG.
- Survives a change. What the documentation says: No floor plan feature is described. How to check it: Send the late change and measure again.
Mark each row yes, partly or no from what you measure. A row the documentation supports can still fail on your room. For the CAD row, ezdxf is a Python library that creates DXF files. The table works for any language model.
What Monet is built to do
Monet builds AI layouts from a venue plan and a brief, in minutes and to scale. It works on a plan from its venue library or on your uploaded PDF or DWG. Its checks cover exit clearance, temporary structures in the main walkway, crowd flow and noise on nearby booths. They also give a carbon estimate per layout and spatial occupancy, and they re-run after every change.
One change regenerates the plan, renders and drawings, so the latest version lives in one project. That project gives 3D renders, walkthrough videos and production-ready plans and elevations. Files export to DWG, DXF, PDF, JPG and GLB. The checks flag breaches early. The venue and the local fire authority still approve the plan.
How to run the checks
Use a room where you know every dimension. Your own venue's DWG works, and so does a simple test hall you draw in CAD. One useful test hall is 60 x 40 m on a single level. Issue it as a DWG in millimeters and as a vector PDF at 1 to 200 with a scale bar. Give it four 2.4 m exit doors, two on each long side. Put a 4 m loading door on the east wall and the main entrance on the west wall. Add two 0.6 m square columns on the center line, 20 m and 40 m from the west wall. Write down ten distances you can measure later, such as the overall size, each exit width and the column positions.
Paste the same brief into both tools, word for word.
"One-day trade show in the attached hall. Peak of 600 people on the floor. 30 booths of 3 x 3 m and four 6 x 6 m island stands. A seminar area of about 12 x 10 m with 100 seats in rows facing a 6 x 3 m stage. Registration at the main entrance with two queues. A catering area of about 150 m2 with 20 high tables. Main aisles 3 m wide, cross aisles 2 m. Keep every exit, and a 2 m path to it, clear. Nothing within 1 m of a column. Priorities in order are exits and aisles, seminar sightlines, island-stand visibility, catering flow."
Give Claude three routes, each from a fresh chat. The reasoning route reads the PDF, lists dimensions and exits, and computes exit capacity and aisle space. The drawing route makes an SVG at 1 unit = 1 mm on the hall outline. The code route writes Python that produces a DXF. Skip the photographic render, because Claude does not make one. In Monet, upload the DWG or PDF and paste the brief. Send each tool the same follow-ups, in order and only where needed.
- "Use the attached plan at its true scale."
- "Make main aisles 3 m and cross aisles 2 m."
- "Keep a 2 m clear path to every exit."
- "Keep everything 1 m from the columns."
- "List every assumption you made."
Last, send both tools the same late change. "Move the seminar area to the opposite end of the hall and add four 3 x 3 m booths. Keep everything else." Then open every output in CAD at full size over the venue drawing. Score each row of the table above from what you measure. Run each generative route more than once, each time from a fresh chat, because answers vary between runs. Keep every file, so a colleague can check your reading.
Which tool for which job
Claude is the better choice for reading long venue manuals, exhibitor packs and tenders. It also suits finding contradictions in a brief and checking the arithmetic behind exits and aisles. Monet is the better choice for the layout itself on the real room at true scale. It also handles the renders and drawings that follow, and a late change that must reach every file.
What this means for planners
Claude questions the brief and the assumptions. Monet produces the geometry, renders and drawings, and a person signs. There is more on that last step in can you trust AI to design your event.
Frequently asked questions
Can Claude create an image of a floor plan?
Claude does not generate photos or illustrations the way image tools do. It can draw a plan as an SVG or HTML visual, and it can write code that produces a DXF. Those outputs are geometry, so you can open them in CAD and measure them against the venue drawing.
Can Claude calculate exit capacity for an event?
It can work through the arithmetic step by step, from the rule you give it and the dimensions in the venue file. The answer is only as right as the rule and the inputs. So check its numbers against the rule your licensing authority uses.
What is Claude best used for in event planning?
It is best at reading long documents such as venue manuals and exhibitor packs. It also finds contradictions in a brief, compares supplier quotes and drafts risk assessments. It is a strong check on the reasoning around a layout. The layout itself needs a tool that works in geometry.
To compare Claude's output with a layout from Monet in your own room, start a free trial and upload the venue's PDF or DWG.