Real-world intelligence isn't limited to one sense. We don't just hear or see—we combine multiple inputs to understand our environment. AI systems should work the same way.
The Limitations of Unimodal AI
Traditional AI systems process one type of data: - NLP models understand text but can't see images - Computer vision sees but can't read context - Speech systems hear but lack visual grounding
This fragmentation misses the richness of real-world scenarios.
Multimodal Capabilities
Tesan AI natively supports multimodal workflows:
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Vision + Language Understand images in context: - "Is there a crack in this component?" - "Count the people wearing hard hats" - "Describe the damage in this photo"
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Speech + Text Natural voice interfaces with text fallback: - Voice commands in noisy environments - Automatic transcription with context - Multilingual support
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Sensor Fusion Combine IoT data streams: - Camera + LiDAR for robotics - Audio + vibration for machinery - GPS + accelerometer for logistics
Architecture
┌─────────────────────────────────┐
│ Multimodal Fusion Layer │
│ (Cross-Modal Attention/CLIP) │
└───────────┬─────────────────────┘
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
┌───────┐ ┌───────┐ ┌───────┐
│ Vision │ │ Text │ │ Audio │
│Encoder│ │Encoder│ │Encoder│
└───────┘ └───────┘ └───────┘
│ │ │
▼ ▼ ▼
Images Text Sound
Use Cases
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Manufacturing Quality Control Camera detects visual defects while microphone identifies abnormal sounds—together catching 40% more issues than either alone.
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Customer Service AI sees customer's product photo, reads their description, and hears their tone of voice for comprehensive understanding.
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Healthcare Combines patient's spoken symptoms, medical images, and EHR text for holistic diagnosis support.
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Retail Visual search ("find similar products") combined with text filters and voice navigation.
Technical Considerations
Synchronization: Aligning inputs that arrive at different rates Missing Modalities: Graceful degradation when inputs are unavailable Computational Cost: Efficient fusion without exponential complexity Edge Deployment: Running multimodal models on resource-constrained devices
Our Approach
We use: - Late fusion for efficiency (separate encoders, combined at decision) - Attention mechanisms for cross-modal alignment - Modality dropout for robustness to missing inputs - Knowledge distillation for edge deployment
The Future
Multimodal AI is the path to more capable, more natural systems. As models like GPT-4V and Gemini show, the future is multimodal.
We're excited to bring this capability to edge and robotic systems where it can make the biggest real-world impact.