How Laan Labs is transforming the way industries document, inspect, and collaborate on critical assets using spatial computing and AI for Rapid Inspection & Documentation (RID).
Every industry faces the same fundamental challenge: how do you rapidly document, inspect, and share information about physical assets? Whether it's an insurance adjuster assessing damage, an engineer inspecting aircraft parts, or a film crew planning a shoot, the questions remain constant: Where is it? What is it? What condition is it in?
Traditional documentation methods are painfully slow, prone to human error, and create silos of information that are difficult to share. Inspectors typically capture hundreds of photos, manually write reports, and rely on memory to connect observations across multiple visits. Critical details get missed, handwritten notes become illegible, and sharing findings with remote teams requires hours of report compilation.
We've spent over years working in AR/VR/XR, watching the technology evolve from experimental to essential. Now, with the convergence of mobile computing power, computer vision, spatial computing platforms like Apple Vision Pro, and modern multimodal AI, we're finally able to deliver on the promise of Rapid Inspection & Documentation (RID).
Laan Labs is transforming asset documentation across industries using spatial computing and AI—from aerospace parts inspection to insurance claims, making comprehensive documentation accessible to everyone.
RID (Rapid Inspection & Documentation) is our systematic approach to capturing, storing, and collaborating on physical asset information. Born from years of experience building computer vision and AR applications used by millions, RID represents the culmination of advances in mobile sensors, spatial computing, and artificial intelligence.
Traditional inspection workflows were fragmented: capture photos, manually transcribe serial numbers, write separate reports, and hope nothing was missed. RID unifies this into a single, intelligent workflow where AI does the heavy lifting.
It's not just about taking photos—it's about creating a comprehensive, AI-enhanced digital twin that combines:
What makes RID transformative is the integration of modern multimodal Large Language Models. These AI systems can:
The beauty of RID is its versatility. We're seeing adoption across industries:
To demonstrate RID's capabilities, let's look at a recent project documenting end-of-life aerospace parts for reuse—specifically, landing gear from the Airbus A318.
The A318 had limited production (only ~80 aircraft built from 2001-2013), but about 60 remain in service, creating ongoing demand for replacement parts. The challenge: how do you continuously document the condition of complex parts while the aircraft is still in service, ensuring accurate information for future reuse?
Using just an iPhone with LiDAR capabilities, field technicians can now:
The result? What once took days of manual documentation now happens in minutes, with far greater accuracy, consistency, and detail.
The breakthrough in RID's capabilities comes from integrating state-of-the-art multimodal Large Language Models that can analyze images with human-level (and sometimes superhuman) accuracy. Our platform leverages multiple LLM providers to optimize for different inspection scenarios:
Our RID platform integrates state-of-the-art multimodal models from leading AI providers including OpenAI, Anthropic Claude, and Google Gemini, enabling capabilities such as:
Beyond these foundation models, we've developed specialized variants:
Defect Detection Models: Fine-tuned on industry-specific datasets to identify:
Part Identification Models: Custom-trained to recognize:
OCR Enhancement Models: Specialized for:
Our RID platform intelligently routes inspection tasks to the most appropriate AI model based on the specific use case, whether it's critical safety inspections requiring maximum accuracy, high-volume documentation needing fast processing, specialized detection with custom fine-tuned models, or real-time field analysis for immediate feedback to technicians.
This flexibility ensures optimal performance, cost, and accuracy for each scenario. Users can also manually select their preferred model based on specific requirements or compliance needs.
The integration of these LLMs transforms inspection workflows:
Our RID pipeline leverages cutting-edge technologies across the entire workflow:
The Apple Vision Pro has transformed how remote teams collaborate on physical assets. Engineers can now review parts in high fidelity from anywhere in the world, with the device's exceptional resolution providing unprecedented detail for inspection tasks.
Combined with our LLM-powered analysis, teams can:
We've even built AirDraw, a spatial drawing app that showcases these capabilities and helps users understand the potential of spatial computing for creative and technical applications.
RID represents more than just a technical achievement—it's a fundamental shift in how organizations think about physical asset documentation. By combining the ubiquity of mobile devices with the power of spatial computing and AI, we're making comprehensive documentation accessible to everyone.
The convergence of technologies we're seeing today—improved mobile sensors, powerful on-device processing, cloud-based AI, and immersive displays—is creating opportunities we could only dream about a few years ago. As these technologies continue to evolve, we're committed to pushing the boundaries of what's possible in spatial computing and computer vision.
This work represents the culmination of over two decades of experience in XR and spatial computing. With 70+ million app downloads and multiple #1 rankings on the App Store (including apps like 3D Scanner App, Face Swap Live, and iVideoCamera), we've learned what it takes to make complex technology accessible and useful.
Want to learn more about how RID could transform your documentation workflows? Contact us at labs@laan.com or visit laan.com.
Computer Vision, Neural Networks, LLM Integration, Spatial Computing, Apple Vision Pro, Mobile LiDAR, Gaussian Splatting, OCR, Cloud Services
Get the markdown source for offline reading