Posted by As Indian enterprises accelerate their digital transformation journeys, legacy modernisation has emerged as both a strategic priority and an operational necessity. Many organisations are grappling with outdated systems that limit agility, slow innovation, and increase costs. To remain competitive in today’s fast-evolving market, businesses must rethink and rebuild their IT infrastructures with modern, scalable, and AI-ready platforms.
In this edition of CTO Talks with TechHerald, MongoDB Field CTO Boris Bialek shares how the company is enabling businesses in India to break free from outdated architectures, embrace AI-ready data platforms, and drive faster, scalable innovation. He also discusses the role of generative AI, the impact of MongoDB’s Voyage AI acquisition, and the key challenges enterprises face while modernising applications.
Edited excerpts…
Q1. What is MongoDB’s strategic roadmap for tapping into the growing legacy modernisation opportunity in India?
Field CTO Boris Bialek: Modernisation in India is more than just a strategic priority — for many companies, it is an urgent necessity. Businesses still operating on legacy architectures face increasing inefficiencies, missed opportunities, and difficulty keeping pace with AI-driven innovation. MongoDB is uniquely positioned to lead this transformation by enabling faster outcomes and unlocking future-ready architectures that can easily scale and evolve.
Our approach centres on breaking free from rigid systems and fragmented architectures to help organisations adopt modern, AI-ready data platforms. A key part of this strategy is assisting businesses in unlocking siloed data, migrating to unified data layers built on our document model, and establishing scalable, future-proof software foundations.
By integrating generative AI tools into MongoDB’s modernisation methodology, we accelerate the refactoring of monolithic applications into modular, microservices-based architectures — doing so much faster and more cost-effectively than traditional methods. For Indian enterprises undertaking large-scale modernisation, MongoDB’s document model and distributed architecture provide the agility and flexibility required to meet rapidly changing demands.
Q2. How is legacy modernisation accelerating business transformation?
Field CTO Boris Bialek: Legacy modernisation is the cornerstone of successful digital transformation. Moving away from rigid architectures allows organisations to reduce operational bottlenecks, unlock real-time data access, and build agile systems that can adapt to ever-changing market demands. Modernisation is not just about upgrading infrastructure; it is about delivering superior customer experiences, fostering continuous innovation, and driving long-term growth.
MongoDB has played a key role in helping leading organisations achieve these outcomes. For example, Zepto, a fast-growing quick-commerce firm, relied on MongoDB to modernise its operations, ensuring real-time inventory visibility, optimised delivery routes and lightning-fast fulfilment — all crucial to succeeding in the hyperlocal delivery market.
Similarly, SonyLIV, a major OTT platform, rebuilt its content management system on MongoDB, reducing search query latency from 1.3 seconds to just 0.03 seconds, a 98% performance improvement, while seamlessly managing half a million pieces of content and scaling during live events with millions of concurrent users.
Intellect Design, a leading fintech company, modernised its Wealth Management platform with MongoDB, cutting onboarding times by 85% and development cycles by 200%, while laying the foundation for advanced AI-driven analytics. These examples demonstrate how modernisation powered by MongoDB enables businesses to innovate faster, scale efficiently and deliver exceptional value in the AI-driven era.
Q3. What’s the role of Generative AI in modernising application and data layers as well as improving the accuracy of GenAI?
Field CTO Boris Bialek: Generative AI radically accelerates legacy modernisation by automating code refactoring and transforming monolithic architectures into modular microservices, reducing manual effort and timelines by up to 10 times compared to traditional approaches. MongoDB uses intelligent orchestration to analyse the full application stack, rewrite code dynamically, and automate regression testing, enabling faster and more reliable modernisation.
Moreover, MongoDB enhances GenAI accuracy by ensuring AI models are grounded in clean, structured and real-time data. Native support for vector search and semantic retrieval provides enterprises with the flexibility and scalability required to build intelligent, context-aware applications, all while reducing complexity and driving faster innovation.
Q4. How is MongoDB leveraging Voyage AI acquisition and what does it mean for its existing enterprise customers?
Field CTO Boris Bialek: Voyage AI brings expertise in applying advanced deep learning techniques to create highly accurate, domain-specific vector embeddings essential for enterprise-grade AI workloads. Their technology enables organisations to generate high-quality embeddings tailored to their unique data, which is foundational for building truly intelligent and context-aware applications.
MongoDB’s acquisition of Voyage AI is a strategic move to deliver end-to-end AI capabilities natively within our platform. By integrating Voyage into Atlas Vector Search and embedded APIs, MongoDB will enable enterprises to perform hybrid search, vector embeddings and real-time LLM interactions from within a single data platform — without stitching together multiple components.
For existing customers, this means faster time to value with unified data and AI infrastructure, reduced complexity in deploying GenAI applications such as semantic search, chatbots and recommendation engines, and improved result accuracy through re-ranking and real-time retrieval from operational data stores. This integration empowers enterprises to build scalable, production-grade AI solutions while maintaining simplicity and trust in their data architecture.
Q5. Which are the key challenges enterprises face during application modernisation? And how can MongoDB help to solve them?
Field CTO Boris Bialek: For enterprises aiming to remain competitive in today’s digital-first world, application modernisation is no longer optional — but the journey can be challenging and complex. Organisational resistance, technical debt and the pressure to deliver rapid value can derail even the best-funded initiatives. We regularly see three main challenges:
Lack of buy-in from executives and development teams: Modernisation often faces scepticism from leadership and resistance from developers accustomed to legacy systems. Without clear proof of value, stakeholders hesitate to invest time and resources. MongoDB advocates starting with small, low-risk incubator projects or proofs of concept that demonstrate measurable business and technical benefits. These early wins build confidence and momentum among both executives and development teams.
“Lift and shift” instead of true modernisation: Many projects simply move monolithic, rigid architectures into the cloud without rethinking the design. This misses the opportunity to improve agility, scalability and user experience — resulting in little or no return on investment. MongoDB encourages re-architecting for a cloud-native, microservices-friendly world using its flexible document model and distributed, scalable platform. This enables genuine modernisation — empowering teams to redesign applications to fully leverage modern infrastructure and patterns.
Project overruns in time and cost: Large-scale modernisation efforts often spiral beyond planned timelines and budgets due to unforeseen complexity and lack of incremental delivery. With its developer-friendly API, flexible schema and managed cloud services (like Atlas), MongoDB helps teams move faster and more efficiently. Its ecosystem reduces operational burden, enables iterative development and keeps modernisation efforts aligned to business priorities and deadlines.
Q6. Compared to other regions like Europe or the US, India is still evolving on the data regulatory and compliance front. So would this have any bearing on data applications and its business impact? What’s your take on this scenario in India?
Field CTO Boris Bialek: While India’s data regulation landscape, such as the Data Protection and Digital Privacy (DPDP) framework, is still maturing, the fundamental business challenges remain the same as in other parts of the world — ensuring secure, compliant and scalable data usage.
In fact, India’s rapid digital adoption and mobile-first ecosystem often demand more innovation at scale. This is why modernising to a more flexible, modular and cloud-ready data infrastructure is a key strategy for businesses operating here.
For example, Intellect AI, a leader in insurance and financial technology, leveraged MongoDB’s flexible, scalable document model and distributed architecture to modernise its applications and meet the demands of India’s fast-changing digital and regulatory landscape.
By using MongoDB, they were able to build cloud-native, privacy-aware platforms that adapt quickly to evolving requirements, implement privacy-by-design principles and stay aligned with global standards without overhauling systems every time rules change. Such modernisation enables companies to adapt rapidly to evolving regulatory requirements, deliver agility, cost efficiency and consumer trust — turning regulatory readiness into a competitive advantage.
(Note: This interview with MongoDB Field CTO Boris Bialek, was conducted over a virtual call with a follow-up via email. Responses have been edited for clarity and brevity.)