D8alytics
D8alytics is a computer vision platform that turns natural-language descriptions into production-ready detection models. Users describe what to detect, generate or upload images, and receive auto-labeled, quality-scored datasets in standard YOLO format. The platform also offers GPU-accelerated training, in-app inference, and API access, with applications ranging from industrial smoke-plume detection to defect classification on production lines.
- Artificial Intelligence
- Developer Tools
- Industrial Automation
- Manufacturing / Industry 4.0
- Software Only
Funding
Founders
Product
Problem
Training accurate computer vision models typically requires large, manually labeled datasets, which are time-consuming and expensive to create. Many teams also struggle with class imbalance, duplicate or blurry images, and the need for specialized infrastructure to train and deploy models, creating a high barrier to entry for non-experts.
Solution
D8alytics provides a computer vision platform that simplifies the entire model development pipeline. Users start by typing a text prompt describing what to detect, and the platform generates photorealistic, auto-labeled images balanced across every class. Alternatively, users can upload their own images, and the system automatically draws bounding boxes, identifies duplicates and blurry images, and splits the data into train, validation, and test sets. Each dataset is scored out of 100 with plain-English explanations, and the platform supports GPU-accelerated training, in-app inference, and API access for deployment. The platform also offers a free plan with up to 6 classes and 15 images per generation, making it accessible for experimentation.
Target Audience
Primary users are machine learning engineers, computer vision developers, and domain experts in industries like manufacturing, healthcare, and energy who need custom detection models without extensive data-labeling resources.
Features
- Text-prompt-based image generation that creates photorealistic, class-balanced training data
- Automatic bounding-box labeling for both generated and user-uploaded images
- Dataset quality scoring out of 100 with plain-English reasons for scores
- Automatic train/validation/test split and duplicate/blurry image detection
- Standard YOLO format output compatible with any existing trainer
- GPU-accelerated model training with live progress tracking
- In-app inference testing and API for integration into external products
- Optional environment and realism variety controls, plus augmented copies for robustness