Multimodal AI development services

Traditional models are designed to process one format of input, and, typically, it’s documents or images. Multimodal models are designed to process multiple types of input (text, images, audio, video, and others), which helps them understand the context before producing an output. 

This enables business leaders to leverage multimodal intelligence where systems can reason across sources.

The training of traditional AI models is straightforward and involves a dataset that involves one type of data. The training of multimodal AI models is not that simple and requires a dataset that combines many types of data into one reliable source of truth.

This allows business leaders to leverage multimodal systems for workflows that require richer context.

• Custom multimodal AI systems can start as smaller, well-defined engagements
• Custom cross-modal AI systems usually require multi-phase investments

• Prototype-level, simpler AI systems are delivered within weeks
• Production-ready enterprise AI systems can take several months

To determine if multimodal AI matches the scenario, we evaluate your needs, data, workflows, and systems. It’s possible that other AI tools are the better choice.

In brief, when providing AI services, we focus on resolving the problem, not following a trend just because.

For ethical AI services, we apply many mechanisms: data encryption, access controls, and other best practices. In our AI projects, we protect sensitive information while delivering instant results in dynamic business settings.

The result is advanced AI systems without compromises.

• Cross-modal reasoning – to connect the insights from different data types
• Joint representation – to unify the modalities into one shared context
• Cross-modal generation – to create one format from another data type
• Modality translation – to convert the information between different data types

• Architecture & encoders
  • Multimodal architectures 
  • Modality-specific encoders
• Integration & alignment
  • Cross-modal alignment 
  • Sensor fusion
• Models & approaches
  • Large language models (LLMs) 
  • Natural language processing (NLP) 
  • Computer vision 
  • Speech recognition 
• Frameworks & tools
  • RAG systems
  • AI agents
  • Vector databases
  • Training collections