Multimodal AI for Indian Knowledge Systems: Beyond Text-Based Intelligence

Artificial intelligence systems have traditionally focused on text. Language models, search engines, and many knowledge systems rely primarily on written data to understand and generate information. However, much of human knowledge, especially within cultural and historical traditions, exists beyond text.

In India, knowledge systems often include visual art, manuscripts, oral storytelling, music, performance traditions, and symbolic imagery. These forms of knowledge carry meaning that cannot always be captured through text alone. To fully represent these systems in the digital world, AI must evolve beyond text-based approaches, and this is where multimodal AI becomes important.

At Bhaskar, exploring multimodal innovation is one of the objectives guiding our work in cultural knowledge systems and AI research.

What Is Multimodal AI?

Multimodal AI refers to artificial intelligence systems capable of processing and connecting multiple types of data simultaneously. Instead of relying only on text, these systems can work with combinations of:

• images

• audio recordings

• video

• manuscripts and scanned documents

• structured metadata

By integrating multiple forms of information, multimodal AI can provide a more complete representation of complex knowledge domains. This approach is particularly valuable when working with historical archives and cultural collections, where meaning often emerges from the relationship between different forms of media.

Why Multimodal Systems Matter for Cultural Knowledge

Many cultural traditions contain layers of meaning that span visual, linguistic, and historical contexts. For example:

• a painting may contain symbolic motifs linked to historical narratives

• manuscripts may combine text with illustrations and calligraphy

• oral traditions may accompany visual or performative practices

Text-only systems struggle to capture these relationships. Multimodal AI, by contrast, allows digital systems to connect these elements and present them as integrated knowledge networks. This capability opens new possibilities for researchers, educators, and cultural institutions seeking to explore complex cultural material.

Applications in Cultural and Knowledge Systems

Multimodal AI can support a wide range of applications related to cultural knowledge. Some examples include:

• Artwork classification and contextual discovery
  AI systems can analyse visual elements of artworks and connect them to historical narratives or related collections.

• Digitised manuscripts and archives
  Combining image recognition with text analysis can help interpret manuscripts that include both visual and written components.

• Cultural storytelling platforms
  Multimedia archives can be presented in ways that combine narrative, visual material, and historical context.

• Cross-cultural knowledge exploration
  Researchers can discover connections across collections that might otherwise remain hidden.

These applications help transform static digital archives into interactive knowledge systems.

Challenges in Multimodal AI

Despite its potential, multimodal AI presents several technical and conceptual challenges. These include:

• limited datasets for culturally specific material

• difficulty interpreting symbolic or stylistic elements

• the need for contextual metadata

• ensuring ethical use of cultural data

Addressing these challenges requires interdisciplinary collaboration between technologists, historians, linguists, and cultural scholars. Human expertise remains essential in interpreting cultural meaning and guiding responsible AI development.

Bhaskar’s Multimodal Exploration

Bhaskar’s work in multimodal innovation connects with several broader research objectives. Our initiatives aim to explore how multimodal systems can support:

• cultural preservation

• digital knowledge representation

• language technology for cultural documentation

• AI systems that reflect complex knowledge traditions

By combining visual, textual, and contextual data, we seek to contribute to digital systems that represent cultural knowledge more accurately and meaningfully.

Researchers, technologists, and cultural institutions interested in multimodal AI, cultural archives, and knowledge systems are invited to connect with us to explore collaborative research opportunities.