PlavnesMachine Learning Research Lab
Building the brains behind autonomous construction
We are closing the gap between digital intelligence and physical assembly. Our neural frameworks and computer vision systems are making residential building fully autonomous.
10x
faster assembly
Accelerating construction timelines through intelligent automation and parallel task execution
+15% YoY
95%
precision rate
Computer vision accuracy in field conditions including dust, rain, and varying light
+3% from 2024
60%
cost reduction
Through automated workflows, predictive maintenance, and optimized resource allocation
Industry leading
Zero
on-site injuries
Safety through autonomous systems with multi-layer collision detection and emergency protocols
Since 2023
Research Areas
Solving construction through machine learning
Neural Frameworks
Deep learning architectures optimized for real-time decision making in dynamic construction environments.
Computer Vision
Multi-modal perception systems that understand spatial relationships, material properties, and assembly sequences.
Edge Computing
Distributed inference pipelines that operate reliably in harsh job site conditions with minimal latency.
Robotic Control
Motion planning and manipulation algorithms for precise, safe interaction with construction materials.
Our Mission
Making housing faster, safer, and more accessible globally
Residential construction is one of the last frontiers for full-scale automation. By solving the complexities of the construction site through ML, we can transform how the world builds homes.
Technology
Where high-level research meets the job site
Our systems are designed from the ground up for the challenges of real-world construction: dust, debris, variable lighting, and the complexity of human collaboration.
- Real-time 3D scene reconstruction60 FPS
- Material detection and classification99.2% accuracy
- Autonomous tool selection150+ tools
- Dynamic path planning<50ms
- Multi-agent coordinationUp to 12 agents
- Safety-critical system monitoring24/7
Neural network active
Our Process
From research to reality
Our end-to-end approach ensures that every algorithm we develop is grounded in real-world requirements and validated through rigorous testing.
Data Collection
We gather millions of data points from real construction sites, including 3D scans, sensor readings, and workflow patterns to train our models.
Model Training
Our proprietary neural networks learn from this data to understand the physics of construction, material properties, and optimal assembly sequences.
Simulation Testing
Before deployment, every model is rigorously tested in our digital twin environments that replicate real-world conditions with high fidelity.
Field Deployment
Our systems are deployed on actual construction sites, working alongside human crews to validate performance and gather new training data.
Capabilities
Built for the real world
Every feature is designed to handle the unpredictable nature of construction sites while maintaining the highest standards of safety and efficiency.
<10ms
latency
Real-time Processing
Our edge computing systems process sensor data in milliseconds, enabling instant decision-making on the job site.
99.9%
uptime
Safety First
Multi-layered safety systems including collision detection, load monitoring, and emergency shutdown protocols.
24/7
monitoring
Weather Adaptive
Our models adjust operations based on environmental conditions including temperature, humidity, and wind.
40%
faster
Predictive Planning
AI-driven scheduling optimizes material delivery, crew deployment, and equipment utilization.
100+
integrations
Modular Architecture
Our systems are designed to integrate with existing construction workflows and equipment.
3x
productivity
Human-AI Collaboration
Designed to augment human workers, not replace them. Our systems enhance safety and productivity.
Publications
Our latest research
Peer-reviewed papers from our research team, pushing the boundaries of autonomous construction.
Autonomous Framing Assembly Using Multi-Modal Sensor Fusion
Chen, A., Williams, J., Patel, R.
We present a novel approach to autonomous framing that combines LiDAR, RGB-D cameras, and force sensors for precise material handling and assembly.
Real-Time Construction Site Understanding via Transformer Networks
Williams, J., Zhang, L., Anderson, M.
A transformer-based architecture for real-time semantic understanding of construction sites, achieving state-of-the-art performance on our new benchmark dataset.
Safety-Critical Control for Construction Robotics
Patel, R., Chen, A., Thompson, K.
We introduce a formal verification framework for ensuring safety in autonomous construction systems operating alongside human workers.
Edge Computing Architectures for On-Site ML Inference
Thompson, K., Garcia, S., Williams, J.
Optimized neural network deployment strategies for construction site edge devices, achieving sub-10ms inference latency.
Collaboration
Working with the best
We partner with leading research institutions and industry pioneers to push the boundaries of what is possible in autonomous construction.
Stanford AI Lab
Research
MIT CSAIL
Research
Berkeley Robotics
Research
Construction Tech Inc
Industry
Global Builders
Industry
AutoBuild Systems
Industry
47+
Published Papers
12
Patents Filed
8
Industry Partners
3
Years of Research
What Others Say
Trusted by leaders in research and industry
Plavnes is tackling one of the most challenging problems in robotics - the unstructured, dynamic environment of a construction site. Their approach is both rigorous and practical.
Dr. Sarah Chen
Professor of Robotics
Stanford University
Working with the Plavnes team has been transformative for our operations. Their systems have reduced our framing time by 40% while improving quality consistency.
Michael Torres
VP of Innovation
BuildRight Construction
The safety record speaks for itself. Since integrating Plavnes systems, we have seen a significant reduction in workplace incidents.
Jennifer Walsh
Chief Safety Officer
National Builders Association
Our Journey
Building the future, one milestone at a time
Foundation
Plavnes was founded with a mission to revolutionize residential construction through machine learning.
First Prototype
Deployed our first autonomous framing system on a test site, achieving 85% accuracy in structural assembly.
Seed Funding
Raised seed funding to expand our research team and accelerate development of our computer vision systems.
Commercial Pilot
Launched our first commercial pilot program with major construction companies.
The Future
Targeting full autonomous construction capability for single-family residential buildings.
Ready to build the future?
Whether you are a researcher interested in collaboration, a construction company looking to innovate, or an engineer who wants to shape the future of building, we would love to hear from you.
Based in London. Working globally.