Unlock Perfect Data Labeling for Your Project
backed by 6 years of our AI development expertise
Future state-of-the-art AI models demand nothing less than unrivaled data labeling
Deepsound helps you create balanced, high-performance datasets, enabling you to build, fine-tune, and deploy state-of-the-art models for even the most ambitious projects.
Case Studies & Success Stories
We've worked with AI teams for the last 6 years, delivering them high-quality data that has made them leaders in their fields.
Data labeling achieving 99% specificity and 98% sensitivity in diagnosing human respiratory diseases.
Advanced data labeling that was used to create a state-of-the-art AI architecture for analyzing low-quality phone calls.
Three years of in-depth research and data labeling for detailed horse behavior analysis & early disease diagnostics.
Top AI Models Demand the Highest Quality Data Labeling
We handle this critical aspect entirely, ensuring the collection of balanced and accurately labeled data to maximize the accuracy of your AI system.
Pose estimation and tracking remains one of the most challenging tasks in computer vision. To reduce sensitivity to occlusion and ensure robustness, it demands meticulously planned and highly accurate data labeling.
3D Pose Estimation
Object classification is a complex data labeling task requiring careful handling of appearance variations caused by lighting, occlusion, different viewing angles, and clutter. For real-world AI applications, the labeled data must generalize well to new or unseen objects and classes and deliver high accuracy across various images.
Object Сlassification
Pixel-precision segmentation enables the most accurate data labeling, significantly enhancing the performance and accuracy of your AI models.
Image Segmentation
Object detection is one of the simplest and fastest methods of data labeling for training your AI model to identify objects in images. Despite its simplicity, it is highly effective and widely used in various AI architectures for accurate object recognition.
Object Detection
ASR technology plays a significant role in business and daily life. However, challenges such as background noise, pronunciation variations, and professional jargon can significantly impact transcription quality. Addressing these issues often requires meticulously tailored data labeling for optimal results.
Automatic Speech Recognition
Public datasets often focus on basic emotions like happiness, sadness, anger, surprise, fear, and disgust. Unfortunately, these datasets are often insufficient for addressing more complex or domain-specific needs. Solving such tasks requires the development of tailored data labeling that captures the nuances of emotions for each specific application, ensuring that AI models can deliver accurate and context-sensitive results.
Sound Emotion Recognition
Environmental sound classification is widely applied in security systems to identify sounds like gunshots and in predictive maintenance to detect anomalies in machinery. This task demands exceptionally accurate data labeling because even minor inaccuracies can reduce neural network accuracy.
Environmental Sound Classification
Unlike computer vision tasks, sound recognition and classification are inherently more complex. These tasks often require non-standard data labeling methods and innovative approaches to effectively handle the nuances and variability of audio events.
Acoustic Data Classification
NLP models are highly sensitive to data quality. Poorly labeled, misspelled, or questionable data can lead to incorrect or inefficient learning, ultimately impacting the model's performance. Ensuring clean, accurate, and contextually relevant data is crucial for optimal NLP outcomes.
Natural Language Understanding and Generation
Using large GPT models for text summarization often isn't cost-effective for many business tasks. High-quality data labeling can optimize this process by enabling the use of smaller, faster, and more efficient models, significantly reducing operational costs while maintaining performance.
Text Summarization
AI for stock trading has been evolving for years. Solely relying on trading candle charts often falls short of achieving precise results. However, implementing specialized data labeling allows neural networks to be trained with high accuracy and trading efficiency.
Stock Trading
Successful data labeling lies hidden in the details
There are no insignificant details in data labeling. The quality of each labeled frame directly impacts the accuracy of your final AI model.