In an era where engineering often means prompting rather than programming, the 200-member tech team at Cashfree Payments is transforming AI from a tool into a teammate. Using GitHub Copilot, orchestrators built over LLMs, and custom implementations of the Model Context Protocol (MCP), the fintech firm is engineering itself.
Many enterprises are already adopting and building MCP servers, as they significantly enhance the capabilities of AI systems. By enabling real-time interaction with diverse data sources and external tools, MCP servers are driving the development of context-aware AI applications.
AI as the New Engineering Baseline
Walk into a Cashfree sprint review, and you’ll likely see developers prototyping workflows with AI assistance in real time. Using Github Copilot, Cashfree has built internal orchestrators that link requirement gathering, code scaffolding and automated testing into a unified pipeline.
“AI has boosted my productivity rather than posing a threat to my job. I use it to generate base code, assist with testing, and even help with documentation. Nearly 30% of my code is now written by AI,” Varun Bothra, a software development engineer at Cashfree, said.
This orchestration saves time and is reshaping the engineering mindset. Repetitive tasks, such as test generation, documentation, and even system migrations, are increasingly being automated. This leads to developers spending less time coding and more time building.
The Transition from Coder to Builder
This shift is intentional. “We don’t call them coders anymore. They’re builders. AI has freed up engineering time to work on higher-order problems, things that actually move the product forward,” Ramkumar Venkatesan, CTO at Cashfree, said.
While larger tech companies often need scale to innovate, Cashfree has embraced a leaner approach. With a tech team of just 200, the team has automated large parts of planning, QA, and release cycles, enabling fast code reviews. The team has even set an internal goal—to triple the rate of ideation and deployment in the coming quarters, by rethinking how they work.
Kubernetes, Kafka and Cloud-Agnostic Engineering
This philosophy extends to infrastructure as well. Applications are deployed on Kubernetes, with intelligent autoscaling capabilities in place for high-load events, such as IPL traffic spikes. However, for stateful, high-throughput operations like UPI or EMI-based payments, Cashfree leans on Kafka.
“Kafka is our async backbone. It helps us avoid blocking resources for payments that require OTPs or banking responses,” Mayank Juneja, architect at Cashfree, explained. “It’s how we ensure reliability and eventual consistency at scale.”
Kafka also helps the company stay cloud-agnostic, an increasingly important consideration in today’s multi-cloud world. Asynchronous workflows extend beyond payments to merchant notifications, service retries and fraud checks.
Cashfree was an early adopter of the MCP, a specification that standardises how LLMs call APIs. This has enabled the seamless integration of Cashfree’s infrastructure into developer tools like VS Code, ChatGPT and Cursor.
The team even built a multilingual WhatsApp bot powered by LLMs and MCP. Today, even a home-based entrepreneur can type, “Generate a ₹10 payment link,” and have it created instantly without needing any dashboard, code, or app.
“We’ve effectively built a no-code interface over payments, powered by LLMs, voice and natural language,” Juneja said. “And it supports many regional indian languages.”
This approach has unlocked new segments, users who were previously excluded from digital fintech because they weren’t developers or didn’t speak English.
The productivity gains don’t end with code generation. Cashfree engineers have built self-healing CI/CD pipelines, where logs from test failures are analysed by LLMs, helping developers pinpoint bugs within seconds.
The same system is now being extended to production debugging and merchant-side issue resolution. By analysing historical ticket data, the AI system can proactively identify, resolve, or recommend fixes for customer issues even before they’re raised.
“All of this reduces our Mean Time to Recovery (MTTR), and that’s one of our most critical KPIs. We’re not just solving problems faster. We’re predicting them,” the CTO noted.
The vision is to develop AI agents autonomously capable of handling debugging, code fixes and even customer service resolutions, escalating to humans only when necessary.
Looking ahead, Cashfree is preparing for a future where agentic commerce, conversational agents that can browse, shop and transact becomes the norm.
“We’re building agents that can talk to other agents. In the future, when a Google chatbot shops for you, we want to be the payment layer that powers it,” Juneja said.
Moreover, while the company currently leverages models from OpenAI and Anthropic, it is eager to adopt India-specific LLMs as it matures, especially for translation, fraud detection, and compliance use cases.
“The idea isn’t to build our own foundational model, but to build the best payment-focused models and agents that know how money flows in India,” Venkatesan said.
The post Beyond Code: How Cashfree Is Turning Developers into AI-Powered Builders appeared first on Analytics India Magazine.
