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Building Adaptiv: AI Products, Global Teams, and the Next Generation of Builders
Adaptiv Admin
Mar 10, 2026
Rendering Home: An AI Film on Ajmer
Rendering Home is an AI-generated film that explores the historical and cultural evolution of Ajmer, the city where I was born and raised. Through generative visuals, the project reimagines different moments in the city’s past, from the formation of the Aravalli mountains to Ajmer’s modern identity. Combining personal memory, historical research, and emerging AI tools, the film experiments with how generative technology can be used to reinterpret place, history, and cultural storytelling.
The Research-Driven Innovation Process
Bhaskar’s research-driven studio process combines cultural inquiry, design exploration, and engineering experimentation to build responsible AI systems and digital knowledge platforms through iterative development cycles
Technology Across Indian & France
Bhaskar operates a global studio network across Paris, Bengaluru, Ajmer, and Grenoble, enabling collaborative research, design, and engineering through distributed teams and follow-the-sun innovation workflows.
Integrating Research, Design, and Engineering to Build Responsible AI
Bhaskar’s studio culture integrates research, design, and engineering into collaborative pods that build culturally aware AI systems and digital knowledge platforms through structured innovation rituals.
Why Collaboration Matters for the Future of AI and Cultural Knowledge
Complex challenges like multilingual AI, cultural preservation, and ethical technology cannot be solved in isolation. Collaboration between researchers, technologists, artists, and institutions is essential. Bhaskar is building an open ecosystem where interdisciplinary collaboration supports the development of inclusive and culturally informed AI systems.
Ethics and Cultural Intelligence in AI: Designing Technology That Understands Context
AI systems increasingly shape how knowledge is interpreted and shared. Ensuring ethical AI requires more than fairness metrics—it requires cultural intelligence. This article explores why context, language, and cultural understanding must become central to AI development, particularly in diverse societies like India.
Multimodal AI for Indian Knowledge Systems: Beyond Text-Based Intelligence
Much of India’s knowledge exists beyond text—in images, manuscripts, oral traditions, and artistic forms. Multimodal AI offers new ways to understand and connect these diverse sources of knowledge. Bhaskar explores how combining language, visual, and cultural data can support richer digital knowledge systems.
AI for the Common Good: Building Technology That Benefits Society
Artificial intelligence has the potential to transform society, but its benefits are not evenly distributed. AI for the Common Good focuses on developing technologies that expand access to knowledge, support cultural preservation, and improve digital inclusion. Bhaskar’s initiatives aim to align AI development with societal benefit.
How Technology Can Protect and Revive Cultural Heritage
Digital tools offer powerful ways to preserve cultural heritage—but preservation requires more than digitization. This article explores how technology, AI, and curated digital platforms can help document, interpret, and revive artistic traditions. Bhaskar’s initiatives focus on building digital systems that sustain cultural knowledge for future generations.
Human-Guided Framework for Building Better Indic Language AI
Developing reliable AI for Indian languages requires more than large datasets—it requires human oversight. UTKARSHINI is Bhaskar’s framework for testing, annotating, and reviewing scraped information with human expertise. This initiative aims to improve the quality, reliability, and cultural accuracy of Indic language datasets used in AI systems.
Responsible AI in India: Why Cultural Context Matters
Responsible AI must reflect the cultures and societies it serves. In India, ethical AI requires attention to linguistic diversity, cultural knowledge, and local contexts. This article explores why cultural intelligence is essential for AI development and how Bhaskar approaches responsible and inclusive technology design.
Building a Culture Stack: Using Technology to Revive India’s Artistic Heritage
Digital technology can do more than archive culture—it can revive it. This article introduces the concept of a “Culture Stack,” a digital infrastructure designed to preserve, document, and rediscover regional art traditions. Bhaskar’s initiatives aim to combine AI, archives, and digital platforms to support cultural knowledge systems.
Why Language Technology Is Critical for India’s Digital Future
India’s digital future depends on technologies that understand its languages. This article explains why language technology is essential for inclusive AI development and how Bhaskar is working to build tools, research frameworks, and collaborative initiatives that strengthen Indic language ecosystems and make AI accessible to millions.
Learning from Panini: Linguistic Structure and the Design of AI Language Systems
Panini’s grammar represents one of the earliest formal systems for describing language. Its rule-based structure offers valuable lessons for AI researchers building language models for Indic languages. Revisiting these linguistic principles can inform more robust computational approaches to morphology, syntax, and semantic interpretation.
Trust, Risk, and Responsible AI in FinTech
AI is transforming financial services through automated risk analysis, fraud detection, and decision systems. However, these technologies introduce new challenges related to bias, transparency, and regulatory compliance. Responsible AI practices help financial institutions deploy machine learning systems that remain reliable, explainable, and aligned with risk management requirements.
Cultural Intelligence: The Missing Layer of AI
As AI systems interact with global audiences, the absence of cultural context becomes more visible. Cultural intelligence in AI involves understanding language, symbols, and communication patterns within their social and historical frameworks, helping systems respond more appropriately across diverse linguistic and cultural environments.
AI-Native Education: Designing Learning Systems, Not Just Tools
AI is increasingly embedded in education platforms, but many implementations treat it as an additional feature rather than a structural change. AI-native learning systems rethink the architecture of digital education by integrating adaptive feedback, continuous assessment, and data-informed instruction into the design of learning environments.
What “Production-Ready AI” Actually Means
Moving an AI model from prototype to production requires more than high benchmark scores. Production-ready systems depend on reliable data pipelines, monitoring infrastructure, integration with existing platforms, and governance frameworks that address model drift, reliability, and responsible deployment in real-world environments.
Why AI Products Require Cross-Functional Teams
AI systems sit at the intersection of product strategy, design, machine learning, and software engineering. Traditional siloed development slows experimentation and fragments decision-making. Cross-functional teams enable faster iteration, stronger alignment between technical capabilities and user needs, and a more cohesive approach to building reliable AI-powered products.
Language Loss in the Age of AI
The future is being written in code, but only in a few languages. As artificial intelligence becomes the gateway to knowledge, work, and governance, the languages it understands will shape who participates in that future. What happens to the cultures, histories, and knowledge systems carried by languages that AI never learns to speak?
The Real Lifecycle of an AI Product
Successful AI products emerge from more than model development. They require structured discovery, thoughtful human–AI interaction design, rigorous experimentation, reliable engineering, and continuous operational monitoring. Understanding the full lifecycle helps organisations move beyond prototypes and build systems that remain reliable and useful in real-world environments.
Building Intelligent Systems: The Rise of AI Application Engineering
AI Application Engineering is the discipline of turning powerful models into real-world products people can actually use. It bridges AI research and practical deployment by integrating models, data pipelines, system architecture, UX design, and scalable infrastructure. In a world where AI is everywhere but not always usable, application engineering ensures systems are reliable, safe, human-centered, and capable of delivering measurable business impact.
Designing AI That Works: How Strategy + Experience Design Creates Real Impact
The real impact of AI emerges when strategic vision and user experience are built in tandem. A strong AI strategy aligns initiatives with measurable business goals, while experience design ensures those systems remain intuitive, transparent, and trustworthy. Together, they transform AI from a technical capability into a scalable, user-centered ecosystem that adapts, learns, and delivers sustainable value.
AI Video & Culture Tech: Scalable storytelling for the modern entreprise
We are moving from “We need a production team” to “We need a storytelling workflow.” AI video systems now enable organizations to generate scalable, personalized, and culturally aligned content at operational speed. From onboarding to institutional storytelling, synthetic media transforms slow manual pipelines into intelligent, automated knowledge engines.
