Unified intelligence across text, images, audio, and video. Building systems that understand and generate content across multiple modalities seamlessly.
AI that sees, reads, listens, and speaks — unified systems that understand content the way humans do.
Ask questions about images, charts, and documents in natural language. Get answers grounded in visual evidence with cited regions.
Transcription, speaker diarization, sentiment analysis, and meeting summarization. Whisper-based pipelines tuned for your domain vocabulary.
Process mixed-media content — PDFs with embedded images, presentations, and web pages — extracting meaning from every modality simultaneously.
Systems that generate text from images, images from text, and video from descriptions. Creative and analytical applications of generative AI.
Identify which modalities matter for your use case. Map the input/output flow — which signals to fuse, when to separate, and where humans review.
Combine specialized models (vision, speech, language) or use natively multimodal models like GPT-4V and Claude Vision. Architecture depends on your latency and accuracy needs.
Align representations across modalities — shared embedding spaces, cross-attention mechanisms, or late-fusion strategies that preserve modality-specific nuance.
Multimodal evaluation requires multimodal metrics. We build custom benchmarks that measure end-to-end quality across all input types.
We've built NLP, computer vision, and speech systems separately — and together. That breadth means we know where modalities complement each other and where they collide.
GPT-4V, Gemini, Claude Vision, Whisper — we integrate the latest multimodal models using battle-tested production patterns.
From audio preprocessing to visual inference to language generation — one team, one architecture, one support channel.
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or email directly: fernandrez@iseeci.com