- Product vision
EventGrid is a collaborative, browser-based event layout tool that combines:
light CAD-style editing
venue/tent planning
sales quoting workflow
map/satellite visualization
AI-assisted layout interpretation and styling
The core value is that a sales rep can go from client inquiry → layout draft → capacity estimate → visual presentation → saved project/quote asset without leaving the app.
- Core product goals
EventGrid should solve these problems well:
Fast layout creation Sales teams need to drag in tents, tables, chairs, stages, bars, dance floors, etc. and get a plan built quickly.
Accurate sizing Objects must have real-world dimensions and snap cleanly to a measurement grid.
Quote support Layouts should drive guest count, rental counts, and event configuration summaries.
Venue visualization Users should be able to see designs over a plain grid or a satellite/base map.
Repeatability Save projects, reuse templates, search by client/event data, and maintain persistent event records.
Team collaboration Multiple internal users should be able to access and edit project files on a shared hosted system.
AI assistance Use AI for:
diagram interpretation from uploads
object skin generation
future smart layout suggestions
- MVP scope
To avoid building a bloated CAD product too early, I’d recommend a phased rollout.
Phase 1 — MVP
Build the core workflow first:
Canvas / editor
Grid-based editor
Zoom, pan, drag
Snap-to-grid toggle
Rotate objects
Resize supported objects
Undo / redo
Layer ordering (forward/backward)
Text labels
Basic shape tool
Measurement / ruler tool
Image upload as overlay object
Library
Tents
Tables
Chairs
Stages
Dance floors
Bars
Restrooms
Lounge furniture
Fencing / barriers
Generic rectangles/circles/polygons
Tent / venue tools
Create tent by:
square
oval
rectangle
polygon
Ask:
dimensions
side count for polygon
Venue boundary creation
Save venue definitions
Business workflow
Client/project metadata:
project number
first name
last name
venue name
event date
address
Search projects
Save layouts as projects
Save layouts as templates
Auto-save after every change
Manual save/version snapshot
Export
PNG export
PDF export
Reporting
Guest count estimate
Object count summary
Table numbering
Map view
Mapbox integration
Toggle:
plain grid
satellite overlay
Phase 2 — Advanced functionality
After MVP is stable:
AI upload-to-diagram
Auto alignment suggestions
Object attachment logic
Smart skins/material generation
Multi-user concurrent editing
Approval/commenting workflow
Quote integration with rental catalog/pricing
CRM or ERP integration
Mobile/tablet optimized field mode
- High-level user experience Entry flow
User opens app and can choose:
New Project
From Template
Open Existing Project
Open Venue
Upload Diagram / Site Plan
Project setup modal
Before canvas loads:
project #
client name
venue name
event date
address
template selection
choose map/grid mode
Editor layout
A strong UI would resemble a lighter mix of:
Figma
Canva
Photoshop layer panel
simple CAD editor
Suggested layout
Top bar
Project name
Save status
Undo/redo
Zoom %
Export
View toggle
AI tools
Left sidebar
Tool palette
Object library
Tent tool
Venue tool
Text tool
Shape tool
Ruler
Upload image
Center
Main canvas/grid
gray neutral working background
optional satellite base
snapping + smart guides
Right sidebar
Properties panel for selected object
dimensions
rotation
layer position
fill/skin/material
attachment settings
count / table number
metadata
Bottom or collapsible side panel
Layers panel
Object list
Counts
warnings / validations
- Core features broken down A. Canvas / editor engine
This is the heart of the product.
Requirements
Infinite or large bounded canvas
Grid rendering
Pan/zoom
Multi-select
Drag and drop
Snap-to-grid
Snap-to-object edges/centers
Rotation handles
Resize handles
Keyboard shortcuts
Layer ordering
Group/ungroup
Copy/paste/duplicate
Lock/unlock objects
Hide/show layers
Undo/redo history
Recommendation
Use a React + canvas-based rendering engine rather than trying to do everything in DOM.
Best fit:
Konva / React Konva for MVP speed
Later, if needed, move heavy scenes to PixiJS or WebGL-based renderer
Why Konva is a good MVP fit:
strong support for drag/drop
shapes
transformations
selection boxes
events
export to image
easier than raw canvas
B. Object system
Every object should be a data-driven entity.
Example object categories
Furniture
Tent structures
Decor
Utility
Stage/audio
Venue elements
Uploaded overlays
Custom shapes
Text labels
Object model
Each object should have:
id
type
subtype
x, y
width, height
rotation
zIndex
layerId
locked
visible
snapEnabled
dimensions in feet/inches or metric
style/skin
metadata
parent/attachment relationship
quantity rules
collision box
anchor points
This data-first model is important because it powers:
rendering
saving
export
AI interpretation
guest count logic
reporting
C. Tent builder
This deserves its own module.
Tent creation flow
Choose shape:
square
rectangle
oval
polygon
Enter dimensions
If polygon:
enter number of sides
Optional:
wall segments
entrances
pole placements
tent orientation
Generate tent footprint on canvas
Internally
Represent tents as:
geometry footprint polygon
anchor points
structural metadata
style/skin config
Later you can add:
pole placement logic
span constraints
sidewall segmentation
subflooring overlays
D. Venue builder
Venues are more than just a background.
Venue model
venue boundary polygon
address
GPS location
default map center
venue obstacles
permanent fixtures
loading areas
entrances/exits
utilities
terrain notes
Venue use cases
create reusable venues as templates
overlay layouts on real locations
persist venue definitions for reuse
E. Attachment system
This is a smart productivity feature.
Examples:
8 chairs attached to a round table
buffet pieces attached into a line
stage stairs attached to stage
tent accessories attached to tent perimeter
Approach
Create a parent-child relationship:
parent object defines anchors
child objects can snap to predefined anchor slots
moving/rotating the parent updates children
For example:
a 60” round table can have 8 chair anchor points evenly spaced
a rectangular banquet table can have preset seating anchors on each side
This is extremely useful and should be in MVP if possible.
F. Auto guest count and table numbering
This directly supports sales.
Auto guest count
Calculate using:
seatable chairs attached to tables
loose seating objects
standing-room assumptions
custom overrides
Table numbering
auto sequential numbering
zone-based numbering
prefix support (A1, A2, B1)
renumber selected objects only
G. Alignment tools
Requested feature is good and practical.
Two modes
Snap-to-grid
on/off toggle
Align objects to grid
batch alignment after objects are placed
Smart alignment tool
Detect selected object types:
align all selected tables
align chairs relative to tables
distribute evenly
align by center/left/top
straighten rows
This can be a contextual panel action.
H. Layers panel
This should work a lot like Photoshop/Figma, but simpler.
Layer features
reorder drag/drop
bring forward/backward
send to front/back
group objects
lock layer
hide layer
rename layer
color tags
filter by type
Suggested default layers
base map
venue boundary
tent
furniture
decor
labels
uploaded overlays
measurements
I. Image overlays
Useful for floor plans, scanned diagrams, logos, seating references.
Needs
upload PNG/JPEG
place on canvas
resize/rotate
control opacity
lock in place
layer order
optional calibration to scale
That last one is important:
user sets two known points
enters real distance
image becomes to-scale
J. AI upload to diagram
Very valuable, but should be designed carefully.
User flow
Upload:
hand sketch
PDF floor plan
aerial screenshot
scanned diagram
AI then attempts to:
detect boundaries
identify tables/chairs/tents/stages
infer scale
convert to editable objects
Technical architecture
This should be an asynchronous processing service:
file upload
preprocessing
vision model extraction
geometry normalization
confidence scoring
editable draft returned to editor
Practical MVP
Do not promise perfect detection. Start with:
boundary extraction
rectangle/circle/polygon recognition
text OCR optionally later
manual confirmation step
K. Smart skins / material generation
This is mostly a presentation layer feature.
Examples:
wood grain table surface
white vinyl tent
black stage deck
turf flooring
linen table coverings
Implementation idea
Each object has:
default vector style
optional texture/skin asset
optional AI-generated texture
For MVP:
use curated material presets first
AI skin generation can be phase 2
Reason: AI-generated textures can become messy and inconsistent unless tightly controlled.
- Recommended tech stack Frontend Core
React
TypeScript
Vite or Next.js
I would choose:
Best choice: Next.js + TypeScript
Why:
server-hosted multi-user app
built-in routing
API routes if needed
auth-friendly
easier deployment
supports admin/search/project pages well
UI
Tailwind CSS
shadcn/ui for modern components
Lucide icons
Canvas/editor
React Konva for 2D editor rendering
optional dnd-kit for complex panel drag/drop interactions
State management
Zustand for editor state
React Query / TanStack Query for server state
Immer for immutable update ergonomics
Forms
React Hook Form
Zod for schema validation
Maps
Mapbox GL JS or MapLibre depending on licensing and cost considerations
Since you explicitly want Mapbox overlay, use Mapbox
Export
PNG via canvas export
PDF via:
server-side PDF composition
or client-side pdf-lib
Backend Language/runtime
Node.js + TypeScript
Framework
NestJS or Fastify/Express
My recommendation: NestJS
Why NestJS:
scalable architecture
clean modules
good for auth, jobs, file processing, admin workflows
strong TypeScript support
Database Primary DB
PostgreSQL
Why:
relational project/client/template data
good GIS support if you later use PostGIS
reliable for search/filtering/project records
ORM
Prisma
Why:
productive developer experience
good TypeScript integration
clear schema management
File storage
AWS S3 For:
uploaded overlays
exported PDFs/PNGs
AI-processed files
template assets
skin textures
Realtime / collaboration
For MVP, you may not need true Figma-style collaboration. Start with:
autosave + edit locking
presence indicator optionally
Later:
WebSockets / Socket.IO
or Liveblocks / Yjs for multiplayer editing
Search
Use PostgreSQL search first. Then, if needed:
Meilisearch or Elasticsearch/OpenSearch
Search fields:
project #
client first name
client last name
venue name
address
event date
AI services For upload interpretation
Computer vision / multimodal model pipeline
Python microservice can help here
For smart skins
image generation endpoint or texture generation pipeline
Architecture recommendation
Keep AI separate from core app:
Node/NestJS app = main business app
Python AI service = diagram interpretation / texture generation
This prevents AI experiments from destabilizing the main product.
Background jobs
Use:
BullMQ + Redis
For:
AI diagram processing
exports
thumbnail generation
template processing
image calibration jobs
Auth
Auth.js or Clerk
enterprise/internal roles later
Roles:
admin
sales
designer
viewer
Hosting Good production setup
Frontend: Vercel or AWS
Backend/API: AWS ECS / Render / Railway / Fly.io
Database: managed PostgreSQL
Storage: S3
Redis: Upstash / ElastiCache
If you want one cleaner AWS-native stack:
Next.js frontend
NestJS on ECS or App Runner
PostgreSQL on RDS
Redis on ElastiCache
S3 for assets
CloudFront CDN
- Suggested architecture Frontend modules
project-dashboard
project-search
template-library
venue-library
editor-canvas
layer-panel
object-library
properties-panel
reporting-panel
export-panel
map-overlay-view
ai-upload-flow
Backend modules
auth
users
projects
templates
venues
objects
layouts
uploads
exports
search
ai
audit
notifications
- Data model outline Main entities User
id
name
role
teamId
Team
id
name
settings
Project
id
projectNumber
firstName
lastName
venueName
eventDate
address
status
templateId
venueId
createdBy
updatedBy
timestamps
LayoutDocument
id
projectId
canvasStateJson
version
thumbnailUrl
mapViewState
countsSummary
autosavedAt
Venue
id
name
address
coordinates
geometryJson
notes
Template
id
name
category
previewUrl
canvasStateJson
Asset
id
type
fileUrl
thumbnailUrl
metadata
LibraryObjectDefinition
id
type
subtype
defaultDimensions
anchorDefinitions
styleDefaults
capacityRules
pricingLink optional
- Canvas state structure
A clean JSON document structure is critical.
Example shape:
type CanvasDocument = { id: string; version: number; units: 'ft' | 'in' | 'm'; grid: { enabled: boolean; size: number; snapEnabled: boolean; color: string; }; viewport: { zoom: number; x: number; y: number; rotation?: number; }; background: { mode: 'grid' | 'satellite' | 'hybrid'; mapbox?: { center: [number, number]; zoom: number; bearing?: number; pitch?: number; }; }; layers: Layer[]; objects: CanvasObject[]; metadata: { guestCount?: number; tableCount?: number; notes?: string; }; };
Each object can be serialized cleanly for save, export, and AI processing.
- UX features that will matter a lot
These sound small, but they’ll make or break adoption.
Must-have interaction details
smooth drag performance
visible snap guides
precise numeric dimension input
keyboard nudging
duplicate with modifier key
rotate with angle entry
right-click context menu
shift multi-select
clear layer visibility
subtle shadow/highlight for selected object
autosave indicator
“unsaved changes” protection
fast template loading
Helpful keyboard shortcuts
Cmd/Ctrl + Z undo
Cmd/Ctrl + Shift + Z redo
Delete remove
Cmd/Ctrl + D duplicate
arrow keys nudge
Shift constrain movement/rotation
Space pan
- Reporting and sales support
This product becomes much more valuable if it helps quoting.
Generated summaries
total guest count
seated guest count
cocktail capacity
number of tables by type
number of chairs by type
stage count
tent dimensions
square footage used
notes and labels summary
Future quote integration
Later, every object can map to:
rental SKU
internal cost
quoted price
setup labor estimate
That turns EventGrid into not just a design tool, but a sales operations platform.
- AI strategy
You mentioned two AI directions:
upload-to-diagram interpretation
smart skins/material generation
I’d strongly separate them.
AI feature 1: Diagram interpretation
More important for workflow efficiency. This should come earlier.
Good MVP version
upload image/PDF
detect primary shapes
convert into editable draft
user confirms and adjusts
AI feature 2: Smart skins
Mostly visual enhancement. Lower business value initially.
Better early version
predefined material palette
later allow “generate texture from prompt”
- Product roadmap recommendation Stage 1: Foundation
auth
projects
templates
venue library
canvas
object library
save/load
autosave
exports
Stage 2: Business workflows
project search
guest counts
table numbering
attachment logic
alignment tools
map overlay
image overlays
version history
Stage 3: AI and advanced polish
upload-to-diagram
smart skins
collision warnings
smart layout suggestions
multi-user collaboration
quote integration
- Team roles needed to build this
A practical early team:
1 product-minded frontend engineer
React editor/canvas
1 backend/full-stack engineer
projects, saves, exports, auth, DB
1 designer
editor UX matters a lot here
1 part-time AI/vision engineer
once upload interpretation starts
1 PM/founder stakeholder
defines actual event workflow and library accuracy
This app will live or die on editor UX, not just backend architecture.
- Biggest technical risks
- Overbuilding CAD complexity
Don’t try to build AutoCAD in version one. Focus on event-sales speed, not engineering perfection.
- Poor canvas performance
Keep object model lean. Virtualize side panels and optimize rerenders.
- Bad save model
If canvas data structure is messy, templates/export/search/versioning will become painful.
- AI overpromising
Upload-to-diagram should always return an editable draft, not a “final truth.”
- Map overlay complexity
Coordinate calibration between layout units and satellite view can get tricky. Treat this as a dedicated geometry subsystem.
- My recommended exact stack
If I were building EventGrid today, I’d use:
Frontend
Next.js
TypeScript
Tailwind CSS
shadcn/ui
React Konva
Zustand
TanStack Query
React Hook Form
Zod
Backend
NestJS
Prisma
PostgreSQL
Redis
BullMQ
Infrastructure
AWS S3
RDS PostgreSQL
ElastiCache Redis
ECS/App Runner
CloudFront
Maps / export / AI
Mapbox
pdf-lib
server-side image export pipeline
Python microservice for AI interpretation
That stack is modern, maintainable, and strong for a multi-user business app.
- Suggested MVP feature list in one clean package EventGrid MVP
login + teams
project dashboard
project metadata
searchable project list
venue library
template library
grid-based canvas editor
drag/drop object library
tents: square, rectangle, oval, polygon
dimensions + side count input
rotate, resize, duplicate
text tool
basic shape tool
ruler tool
image upload overlay
snap-to-grid toggle
align-to-grid actions
layer panel
attach chairs to tables
guest count summary
auto table numbering
autosave
manual save/version
export PNG/PDF
Mapbox satellite overlay mode
That is already a very compelling first product.
- Positioning statement
A clean marketable way to describe it:
EventGrid is a lightweight event layout and tent planning platform that helps sales and operations teams design, size, visualize, and quote event setups faster.
Or more directly:
EventGrid is modern event CAD for tenting, venue layout, and sales quoting.
- Final recommendation
The smartest way to build this is:
treat it as a layout editor first
a sales workflow tool second
an AI platform third
That order matters.
If you nail:
fast drag/drop editing
precise dimensions
object attachment
templates
autosave
export
satellite overlay
then EventGrid will already feel valuable before the AI pieces are fully mature.