Multimodal Model Development

We architect and deploy models that simultaneously process multiple data types—text, image, audio, and video—for unified perception, analysis, and response across diverse enterprise use cases.

Vision-Language Interfaces

We implement systems that understand screenshots, diagrams, and documents alongside textual context to power use cases like smart search, compliance review, and visual Q&A.

Multimodal Retrieval and RAG

Integrate multimodal retrieval-augmented generation (RAG) to enable models to find and reason over visual and textual sources in real-time. Reduce hallucinations and improve accuracy for knowledge-intensive tasks.

Speech and Audio Intelligence

We engineer systems that combine spoken input with visual or contextual cues for smarter voice assistants, call analysis, and audio-based monitoring.

Cross-Modal Embedding and Representation Learning

Create shared embeddings across modalities for efficient similarity search, classification, and tagging. This enables cross-modal intelligence—like finding documents based on voice, or videos based on text.

Context-Aware Multimodal Agents

Build agentic systems that reason across video, voice, text, and images to deliver dynamic, conversational interactions with memory, real-world awareness, and task coordination.

Multimodal Content Moderation and Compliance

We implement AI filters that can detect and flag policy violations across images, voice, and text—ensuring safe, inclusive, and compliant experiences for both internal and customer-facing systems.