SYNETIC.ai
What You Get
Everything you need for production deployment, no ML expertise required
Custom Trained Model
The Synthetic Dataset
Integration Support
Retraining if Needed
How It Works
From discovery call to production deployment in 2-3 weeks
Discovery Call
Tell us what you need to detect, your deployment environment (cloud/edge/mobile), and performance requirements. We’ll define success criteria together.
We Generate Perfect Data
We create custom synthetic training data with multi-modal sensors (RGB, depth, thermal, LiDAR, radar) tailored to your specific use case. At our expense.
We Train Your Model
We train and optimize a custom model using your preferred architecture. Tuned for your specific deployment constraints (latency, memory, accuracy).
We Test on Your Data
Send us your real-world validation images. We run inference and share the results with you. See exactly how the model performs on your actual data before you pay anything.
We Iterate Until It Works
If results don’t meet the success criteria from Step 1, we iterate on the dataset and retrain at no cost. We keep going until the model performs as promised.
You Pay, We Deliver
Once testing on your data confirms success criteria are met, you pay $50,000 ($25,000 for validation partner). We then deliver the full model + dataset + integration support.
Why Not Build It In-House?
Compare the traditional approach to working with Synetic
Traditional Approach
Time to Production
6-18mo
Total Cost (team + data + compute)
$500k+
Typical Model Accuracy
70-85%
Synetic Approach
Time to Deploy
2-3 weeks
Total Cost (everything included)
$50k
Proven Model Accuracy
90-99%
10-40x Faster, 90% Cheaper, 34% More Accurate
Skip the hiring, data collection, and labeling headaches.
Get a production-ready model backed by university research and a performance guarantee.
University-Verified Performance
Independent validation by University of South Carolina
34%
Better mAP than real-world data
7
Model architectures tested
100%
Tested on real-world validation
2-3
Weeks to production
Models trained on our synthetic data don’t just match real-world performance—they exceed it by 34%. This isn’t theory. It’s peer-reviewed research from USC testing 7 different architectures on real-world validation data.
Your model will outperform real-world trained alternatives.
Technical Specifications
Enterprise-grade models optimized for your deployment environment
Discovery Call
Export Formats
Optimization Options
Performance Targets
Training Data Included
Documentation
Deployment & Integration
Deploy anywhere—from cloud to edge to mobile. Integration takes minutes, not weeks.
AWS, GCP, Azure-optimized models with Docker containers and serverless options included.
Optimized for NVIDIA Jetson, Intel NUC, Raspberry Pi, and custom embedded systems.
iOS and Android optimized models that run on-device with minimal battery drain.
Simple Integration
Python Inference Example
import torch from your_model import YourModel
# Load the trained model
model = YourModel.load(‘synetic_model.pt’)
model.eval()
# Run inference
image = load_image(‘test.jpg’)
detections = model(image)
# Results include bounding boxes, classes, confidence scores
for det in detections:
print(f”Class: {det.class_name}, Confidence: {det.confidence:.2f}”)
print(f”BBox: {det.bbox}”)
We Provide:
Perfect For Teams Without ML Expertise
We handle the entire ML pipeline—you focus on your product
Autonomous Vehicles
Custom perception models with LiDAR + camera + radar fusion. Optimized for edge deployment. We handle sensor calibration, multi-modal training, and real-time inference optimization.
Typical: Full perception stack in 3 weeks
Robotics
Object detection, navigation, and manipulation models. Trained with perfect depth data and multi-modal sensors. Optimized for embedded systems and real-time constraints.
Typical: Custom YOLO model for edge in 2 weeks
Aerospace & Defense
Target recognition with multi-spectral, thermal, and radar. Geospatial intelligence integration. Models optimized for classified deployment environments.
Typical: Multi-spectral detection model in 3 weeks
Manufacturing
Defect detection with thermal imaging. Quality control automation. Models optimized for factory floor deployment with minimal hardware.
Typical: Thermal defect detector in 2 weeks
Agriculture
Crop monitoring with multi-spectral imaging. Disease detection and yield prediction. Models optimized for drone and field deployment.
Typical: Multi-spectral crop model in 2 weeks
Construction
Safety monitoring, equipment tracking, progress monitoring. PPE detection with thermal imaging. Edge-optimized for job site cameras.
Typical: Safety monitoring model in 2 weeks
If the model doesn’t meet the contractually-defined performance criteria in your production environment, we’ll work with you to fix it. We’ll iterate on the dataset, retrain the model, and adjust the architecture until it works.
If after reasonable iteration we still can’t meet the criteria, then you get a full refund..
We only succeed when you succeed.
Frequently Asked Questions
Do I need ML expertise to work with you?
No. That’s the entire point of this offering. You tell us what you need to detect and where you’ll deploy it. We handle data generation, model training, optimization, and integration. You just need to know your use case and deployment constraints.
What if I don’t have a current model or dataset?
Perfect. We generate everything from scratch. You just need to describe what you want to detect and provide some example scenarios or edge cases you care about.
When do I pay?
You pay ($50,000 standard, $25,000 validation partner) after initial testing confirms the model meets success criteria but before we deliver the full model weights and dataset. Once paid, we deliver everything: model, dataset, integration support, and documentation. Then your 90-day guarantee period begins—if it doesn’t perform in production as agreed, we iterate to fix it. If we can’t solve it after reasonable effort, full refund.
What’s the difference between standard and validation partner pricing?
Standard ($50,000): Full custom model, dataset, integration support, and performance guarantee.
Validation Partner ($25,000): Same everything, but you agree to be featured in our next USC peer-reviewed study and get early access to new capabilities. Only 10 spots available.
Can you optimize for edge devices?
Yes. We can optimize and quantize models for edge deployment (NVIDIA Jetson, Intel NUC, mobile phones, custom hardware). We provide TensorRT, ONNX, and TensorFlow Lite exports with performance benchmarking.
What architectures do you support?
We can train any architecture you prefer: YOLO (v5-v11), Faster R-CNN, Mask R-CNN, EfficientDet, custom architectures, or we can recommend the best fit for your use case and deployment constraints.
Do you support multi-modal sensor fusion?
Yes. We train models with RGB, depth, thermal (IR), LiDAR, and radar with perfect sensor alignment. Essential for autonomous systems, robotics, and defense applications where sensor fusion is critical.
What if the model degrades over time?
We include 90 days of post-deployment monitoring and support. If performance degrades, we’ll work with you to understand why and retrain if needed. You also get the full synthetic dataset to retrain yourself.
How is this 34% better if you’re using synthetic data?
USC researchers compared models trained purely on our synthetic data against models trained on real-world data. When tested on real-world validation sets, the synthetic-trained models achieved 34% higher mAP50-95. The physics-based rendering eliminates the “domain gap” entirely by covering the real world variations and removing human error.
Can you handle custom requirements?
Absolutely. We’ve built 150+ models across dozens of industries. Custom sensor configurations, unique deployment constraints, specific accuracy requirements—if you can describe it, we can build it.
Ready to go from use case to production model?
50% Off Validation Partner Program
$50,000 → $25,000 for qualifying research partners
Questions? Email sales@synetic.ai