Leading Indian Organizations Are Driving India’s AI Future With MongoDB

Millions of Indians now use AI on a daily basis. It’s been said before, but the technology’s possibilities are incredible, whether you’re using AI to build an app with just a few sentences, or whether a virtual assistant is not only answering your questions but actually anticipating your needs.

But moving from a great idea to an AI product or feature that works—and that can be used by millions of users—isn’t easy. For every successful AI use case, there are hundreds of stalled or failed projects, and most of the time, this is due to the data foundations powering AI workflows. 

According to IDC, almost half of Indian organizations (46%) think that their existing architecture makes it impossible to build new applications. Specifically, rigid legacy architectures lack the flexibility and scalability to handle the high volume of unstructured data required for AI success.

Meanwhile, forward-thinking leaders are taking another approach: they are modernizing their data infrastructure and investing in data platforms that have been built for AI. This is the case of six leading Indian organizations that are using the flexibility of the document model and the power of the MongoDB platform to take AI from prototype to production at scale.

Speaking at the MongoDB .local Bengaluru event in June 2026, these six organizations shared how they are using the power of the MongoDB platform to remove complexity, gain speed, quickly move from AI prototype to production, and to scale. Here are their stories! 

Emergent Labs unleashed the power of vibe coding with MongoDB Atlas

Emergent Labs uses MongoDB to enable anyone without a programming background or technical knowledge to build production-ready applications using natural language prompts. Through “vibe coding”, autonomous AI agents write, test, and iterate on application code based on user feedback. And they are doing all of that using MongoDB Atlas.

Moving away from traditional relational databases, including PostgreSQL, and onto MongoDB’s flexible document model was a game-changer. It allowed Emergent Labs to rapidly adapt applications as customer requirements evolved, allowing them to iterate without the constraints, complexity, or delays typically associated with rigid schemas. And most importantly, it helped Emergent Labs scale: the company has now powered the creation of nearly 12 million applications across 180 countries, including hundreds of thousands of apps deployed in production.

Munkud Jah, Founder and CEO of Emergent Labs, said: “We saw a lot of our early competitors get stuck in migration loops where database operations would be stuck. One of our competitive advantages early on was using MongoDB, and we have been scaling on MongoDB ever since. We also believe that MongoDB is the best database for agentic coding.”

Zomato sets a new standard for AI-driven customer support with Nugget

Zomato has used MongoDB Atlas to build Nugget, an AI-powered customer support platform that has transformed its operations. Less than four months after launch, Nugget has improved human-agent efficiency by 40%.

Now, with MongoDB Atlas Stream Processing and MongoDB Change Streams, Zomato can perform lightweight, real-time data transformations on the fly using stream aggregation pipelines, instead of having to write and maintain separate data-transformation jobs. It can also perform automatic scaling without manual intervention or grouping collections, so Nugget can keep growing and expanding without adding more architecture complexity or impacting performance.

Leena AI scales virtual AI assistants in a secure environment

Leena AI’s AI-first enterprise employee experience platform serves over 500 global enterprises and millions of employees. Using MongoDB Atlas, Leena AI’s agentic AI platform is able to handle high-volume operational queries and ticket resolutions in real-time—the company’s virtual assistants can quickly retrieve relevant contextual policy details to resolve employee requests instantly.

MongoDB was chosen by Leena AI because it was built for AI: “You can’t have an AI strategy without a data strategy. And to do Agentic AI well, you need a document store, a document retrieval, a hybrid search engine, vectors, embeddings, and reranking,” said Mayank Kapoor, Senior Vice President for Lenna.ai. “You can have different databases doing these things, but then you have to make sure all these databases are in sync so the AI agents can have access to the latest information. Having everything in one single place is a huge help, and that’s what helped our velocity, too.”

Deployed across international enterprise environments, MongoDB Atlas also ensures that the platform scales smoothly under sudden spikes in employee ticket volume, all while maintaining multi-region security and operational performance.

Brahma AI orchestrates complex agentic AI workflows with MongoDB

Brahma AI uses MongoDB Atlas as the intelligent, unified data foundation behind its enterprise AI platform that manages, creates, and distributes content—Brahma AI Core turns assets into structured, high-fidelity data, enabling faster search, seamless collaboration, and automated delivery across workflows, while Brahma AI Studio provides advanced AI creation capabilities so organizations can train brand-specific models and integrate third party audio and video generation systems within a secure, governed framework.

“We were born out of the world's most demanding industries—media, sports, entertainment—where scale, quality, speed, and accountability are non-negotiable,” said Venkatesan J, Senior Enterprise Architect and Head of Product Engineering, Brahma AI. “A platform like MongoDB enables faster development, and we don’t have to worry about rethinking RTOs and RPOs again and again. Our developers can focus on the business needs, instead of spending more time debugging outdated systems.”

Observe AI takes AI-driven contact centers to the next level with powerful search capabilities

MongoDB Atlas is the foundational database layer for Observe AI’s AI-driven contact center and conversational analytics platform, which handles millions of customer service interactions, voice calls, and real-time transcripts. Observe.ai uses MongoDB to ingest, analyze, and store all this high-volume, unstructured conversational telemetry.

“We chose MongoDB because it was a natural fit for the data we have—we need to extract a lot of insights and intelligence from raw conversations. Secondly, it’s all about the flexibility: we don’t want our engineers to spend too much time changing schemas, we need to be able to iterate fast,” said Jithendra Vepa, CTO and Co-Founder, Observe AI. “MongoDB really sits nicely between our data layer and the orchestration layer, and we were able to scale enormously over the years, all while keeping trust and security.”

Gnani AI scales its conventional CX platform across 40+ languages

MongoDB has been instrumental in powering and scaling Gnani AI’s agentic conversational and voice-first customer experience platform. The flexibility of MongoDB Atlas means that Gnani AI can seamlessly manage unstructured data, including millions of voice calls, real-time analytics, and automated workflows across 40+ languages.

Gnani AI has deployed close to about 1,000+ AI agents that handle calling for its customers across a range of industries, including highly regulated sectors such as financial services.

MongoDB has helped Gnani AI manage the data complexity that comes from operating so many agents. MongoDB Atlas’s flexibility means that the company can seamlessly handle different agents that operate with different schemas. Furthermore, MongoDB’s built-in security capabilities ensure that Gnani AI’s data stays in a safe and secure environment.

With built-in capabilities including MongoDB Search, MongoDB Vector Search, and Voyage AI, MongoDB Atlas is built for powering AI-driven use cases. No need to add complexity by using disparate systems for search, retrieval, and embeddings—Indian organizations like Emergent Labs, Zomato, Leena AI, Brahma AI,  Observe AI, and Gnani AI are examples of what combining great tech talent and forward-thinking organizations with the right technology can achieve.