Infosys has partnered with US-based AI company Anthropic to build and deploy enterprise-grade AI solutions across telecommunications, financial services, manufacturing, and software development, with a focus on regulated industries.
The partnership begins with the establishment of a dedicated Anthropic Center of Excellence (CoE) in the telecommunications sector.
CoE helps you build and deploy AI agents tailored to your industry-specific operations.
Under this partnership, Infosys will combine Claude models, including Claude Code, with its Topaz platform to support enterprise AI adoption.
Dario Amodei, CEO and co-founder of Anthropic, said there is a huge gap between AI models that work in demos and AI models that work in regulated industries, and if the industry wants to close that gap, it needs domain expertise.
“Infosys has just such expertise across critical industries such as telecommunications, financial services, and manufacturing. Our developers are already using Claude Code to accelerate their work and create AI agents for industries that require accuracy, compliance, and deep domain knowledge,” Amodei added.
The companies aim to automate complex enterprise workflows, modernize legacy systems and accelerate software engineering in areas where governance, transparency and compliance are non-negotiable.
Agenttic AI moves into the enterprise mainstream
Unlike traditional AI co-pilots that assist with prompts and responses, the Infosys and Anthropic collaboration focuses on agent AI.
Infosys uses tools like Claude Agent SDK and Claude Code to help clients build AI agents that can process insurance claims, conduct compliance reviews, manage network operations, and generate, test, and debug production code.
The goal is to move AI beyond task assistance to persistent end-to-end workflow execution.
For telcos, the first sector targeted by this partnership, AI agents can help modernize network operations, streamline customer lifecycle management, and enhance service delivery.
The collaboration has since expanded to financial services, with AI agents supporting faster risk detection, automating compliance reporting, and providing personalized financial advice based on contextual data.
In manufacturing and engineering, the companies will focus on accelerating product design cycles and simulation-driven development to shorten research and development timelines.
In software development, teams use Claude Code to write, test, and debug code, helping developers move from design to production quickly.
Infosys has already implemented Claude Code within its Exponential Engineering organization, building internal expertise and best practices that directly inform customer engagement.
Strategic signals in India’s AI race
For channel partners and enterprise customers, this in-house implementation signals that they are ready for a production-grade rollout rather than a pilot experiment.
A dedicated Anthropic CoE will develop AI accelerators, industry templates, and governance frameworks for regulated sectors.
The partnership comes as the global AI model provider increases its focus on India.
Anthropic recently opened an office in Bangalore as India emerges as Claude’s second-largest market globally.
Partnering with Infosys gives Anthropic direct access to one of the largest enterprise customer bases in the region.
Infosys CEO Salil Parekh said AI is not only transforming business but also redefining the way industries operate and innovate. Our collaboration with Anthropic marks a strategic leap forward in the evolution of enterprise AI, enabling organizations to unlock value and become more intelligent, resilient, and responsible.
“From modernizing financial services with intelligent risk management and compliance to enabling engineering companies to take the lead in AI-driven design and manufacturing, our goal is to leverage the joint expertise of Infosys and Anthropic to accelerate the realization of AI value for global enterprises,” Parekh added.
For Infosys, this partnership deepens its AI stack at a time when large enterprises are moving from AI proof-of-concept to large-scale deployments embedded in core operating models.
