case study - Cursor AI Hackathon 2026 - Hamburg
Ship the BIM
Reimagining BIM Readiness Validation through AI and Fix Pack Automation
RoleProduct Designer
Timeline48 hours
DomainArchitecture, BIM, AI
ToolsFigma, Miro, research, interviews
Team05
Primary Dashboard interface in real time
Background
In international BIM workflows, model submissions often fail due to non-compliance with local standards. These rejections typically arise from missing data, incorrect classifications and inconsistent terminology.
We discovered that most BIM teams don't know their model is invalid until it's too late when the submission fails. The last-minute scramble costs time, trust, and quality.
The Challenge
Teams face three repeating blockers:
Missing properties like FireRating or DoorType
Inconsistent naming across models and levels
Manual rework with CSVs, checklists, and no clear next step
Despite having powerful modeling tools, BIM professionals lacked a clear way to assess if a model was submission-ready.
Our Objective
To design a pre-submission validation tool that:
Diagnoses readiness with a clear score
Surfaces issues categorized by severity
Uses AI to explain what's wrong, why it matters, and where it lives in the model
Outputs a Fix Pack that's usable by anyone, not just Revit or Archicad experts
The Solution
Ship the BIM sits between your modeling tools and submission systems. It doesn't edit models; it validates, explains, and empowers.
Core Features
Upload & Profile: Upload an IFC model, select the correct master profile (e.g., Germany DIN)
Validation Engine: Parses the model and assigns a Readiness Score
AI Suggestions: every issue is explained with 'What, Why, Where' guidance
Fix Pack Docs: Download a structured CSV, PDF, or DCF-ready package for each scan
Terminology Mapping: Flag mismatched room names or attributes; fix them before submission
Process Methodology
Architectural efficiencythrough intelligent integration
A look into how we streamlined the design lifecycle by embedding AI at key friction points, speeding delivery without losing structural clarity.
How we used AI to move faster
01
Discovery & Synthesis
Used LLMs to cluster interview notes faster, so we could pivot earlier without waiting days to "finish research".
02
Information Architecture
Generated edge-case paths (signal loss, reroutes, mismatch flows) and validated the logic against real delivery steps.
03
Content & Microcopy
Explored tone variants for voice prompts + error states, then rewrote with real delivery context (no generic copy).
04
Prototyping & Iteration
Accelerated layout options + interaction variations, keeping human focus on motion clarity and safety.
05
Quality Assurance
Used structured checklists for states + accessibility: glare readability, contrast, touch targets, offline behavior.
Decision 1
No App Switching: Unified Readiness Flow
WHY
User's hate switching between DVM, system console and applications.
Final solution
During the flow, upload 4 docs for DVM, 2 reviews on System to skip.
Impact
Reduced users' time, more convenience.
Trade-off
Hardcore users lose some power, in some cases it will block out one.
Decision 2
Score-First Readiness View
WHY
Users want to see score first, then details.
Final solution
A 2 tab scrolling view, issue breakdown by category.
Impact
Faster grasp for issues and scope.
Trade-off
Minority group loosing ability with some personas.
Decision 3
What / Why / Where AI Panel
WHY
Users don't know the exact "AI" model used in the backend.
Final solution
Many users' queries are specific to a given task, are answered by LLM's.
Impact
Better knowledge with more confidence, more accurate options.
Trade-off
Maintaining clarity and consistency in product across personas.
Decision 4
Fix Pack Export, Not Auto Fix
WHY
DVM shows incorrect, value-less fix.
Final solution
Offer "Fix Pack" export, which will change bug and give value.
Impact
User's don't get "bad" experience.
Trade-off
Extra effort for users for fix/feedback process.
Validation
The product was tested on a real German office tower IFC.
Detected 30+ issues instantly
Jumped from 62 → 100 in one rescan
Fix Pack included 3 AI recommendations, 2 glossary mismatches, 5 missing property rows
Outcome
Ship the BIM created a new category: readiness tooling for BIM delivery.
Validated by 4 AEC professionals
100% completion rate in our demo loop
Clear interest in integrating with Autodesk Construction Cloud & Solibri
It's not a dashboard. It's not a viewer. It's the gate between "looks fine" and "is actually ready."
We shipped clean, scalable UX that respects complexity and gave control back to the humans doing the work.
0 likes · 0 views · Nov 12, 2026
AI-driven pre-submission BIM validation: score-first readiness, issue explanations (What/Why/Where), and Fix Pack exports; validated with 30+ issues and improve
