As enterprises rapidly scale up artificial intelligence (AI) adoption across operations, AI and SaaS startups are betting big on a new way to deliver results without the cost and complexity of traditional AI systems — modular AI.
Modular AI basically refers to pre-built, function-specific components that can be used individually or assembled together to automate or optimise business workflows. These modules are designed for tasks like fraud detection, natural language processing, and sales forecasting, which can be plugged into existing systems one at a time, making them easier to deploy, manage and upgrade. This plug-and-play nature means companies can add or swap modules without rebuilding entire systems. Unlike large, single-purpose AI models that often require full-scale implementation, modular AI allows companies to pick and use only what they need, when they need it.
Modular AI key features
Modular AI is increasingly preferred by enterprises because it offers speed, flexibility and affordability. It allows businesses to start small, scale gradually, and avoid expensive overhauls of their existing tech infrastructure. With AI models & frameworks evolving at a breakneck pace, this approach helps organisations stay nimble.
“The modular approach is the only way to keep up with how fast the AI space is moving,” Girish Raghunath, senior director-engineering at Gupshup, told FE.
“LLMs (large language models) and frameworks evolve quickly. We knew we needed a system that could keep up. So we designed it to be plug and play. That’s why we are investing more in this approach. It gives us speed, flexibility, and better outcomes for our clients,” Raghunath added.
Startups are expanding their modular AI offerings
Startups like Gupshup, Zoho, Gnani AI, CoRover AI and Sima AI are at the forefront of this shift, and many are expanding their modular AI offerings in response to surging demand. From multilingual chatbots to predictive analytics and voice-based biometrics, enterprises are using these ready-made AI components to solve problems ranging from compliance to agent productivity.
SaaS giant Zoho says modular AI is central to its strategy. Its Zia-branded AI modules are embedded across more than 55 Zoho products, handling everything from sentiment analysis to time-series forecasting. “This right-sizing approach lets customers tap AI everywhere in their workflow without paying for compute they don’t need, and it keeps Zoho’s long-standing privacy promise intact because no customer data is ever fed back into model training,” Ramprakash Ramamoorthy, director of AI research at Zoho, said. The company processes about 16 billion API calls monthly through its modular AI services, with annual growth touching 50%.
Zoho’s modules are trained narrowly and outperform larger LLMs in both cost and speed, making them ideal for everyday use cases that don’t require generative reasoning.
Meanwhile, conversational AI startup Gnani AI, which is also part of the IndiaAI Mission, is also doubling down on modular AI. “The demand for AI without rip-and-replace IT is skyrocketing,” Ganesh Gopalan, co-founder and CEO, said. Gnani plans to expand its modular libraries into industry-specific workflows with deep integrations across CRM (customer relationship management), loan management, and customer engagement platforms. The company has seen strong traction in BFSI, telecom, and BPO sectors, where clients often start with one module and expand quickly into full-stack deployments.
Modular AI’s global appeal is also helping startups expand overseas without the need for custom solutions. In India, clients value its affordability and multilingual support. In West Asia, accuracy and conversational quality take precedence. And in Latin America, where AI adoption is racing ahead, enterprises are quick to experiment and scale.
CoRover AI, which provides modules for NLP (natural language processing), speech translation, and integration services, claims 70-100 million monthly active users, with nearly half of them being new users. The company’s modular stack powers everything from text-to-speech interfaces to backend APIs across large- and mid-sized enterprises. “We have been serving large enterprises, but there is also increasing interest from high-growth, nimble companies due to the convenience offered by modular AI,” Ankush Sabharwal, founder and CEO, said.