Use case
Multimodal reasoning
Unlock a new generation of AI capabilities by generating high-quality data and performing expert-led human evaluations to train your models on text, images, video, and audio data
Why Labelbox for multimodal reasoning
Create high-quality datasets
Use advanced tooling, on-demand experts, AI, and real-time quality metrics to generate high-quality data.
Accelerate time to value
Rapidly integrate data, create quality training data, and deploy models to production.
Access on-demand expertise
Highly-skilled labeling services, data science support, and industry insights available on-demand.
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on labels and quality via Labelbox platform.
Understanding the multimodal landscape
Multimodal reasoning represents a significant leap forward in AI use cases, enabling machines to comprehend the world through a combination of text, images, video, and audio. This capability opens the doors to new virtual assistants, content creation, education platforms, and more.
Challenges of multimodal reasoning
Building effective multimodal AI models requires diverse datasets that encompass a wide range of modalities. However, collecting, annotating, and managing such data can be complex and time-consuming without the right tools or human experts available to capture the nuances of audio, video, and images.
Build next-generation AI with Labelbox
Labelbox has a long history of supporting complex image, text, audio, and video labeling with our industry-leading software. Our platform enables seamless collaboration, efficient annotation workflows, and real-time quality control to help you build state-of-the-art multimodal models.
Customer spotlight
Labelbox's intuitive tooling coupled with post-training labeling services offered a collaborative environment where Speak's internal team, along with external data annotators, could work together seamlessly. Learn more about how Speak uses Labelbox to improving the quality and efficiency of their data labeling.
