Kris@Work, the AI-native Go-To-Market (GTM) execution platform for enterprise revenue teams, has raised $3.0 Million in seed funding led by Infoedge Ventures, with participation from JN Capital & Growth Advisory (Singapore) and several angel investors.
Kris@Work is developed as the new system-of-work and system-of-insights for revenue teams, designed and built as their work companion. designed to eliminate dependency on multiple point tools across sales, customer success, services, support and revenue operations. Founded by experienced SaaS professionals who have built and operated enterprise products, Kris@Work was conceived as a single, unified platform for GTM teams. The founding team includes co-founders Arun Singh and Ramakrishna Mallya, with strong product leadership from Ananta Joshi (Chief Product Officer).
The company moved from the first line of code to its first enterprise contract in under seven months, signalling strong demand for AI-native execution platforms. Kris@Work operates in a rapidly evolving category of enterprise AI platforms for GTM and revenue execution, alongside a new generation of global players rethinking how sales, marketing, and customer success teams operate in an AI-first world.
The company plans to deploy the fresh capital toward expanding its enterprise customer base, strengthening go-to-market partnerships, and completing development across all four phases of the platform, with a focus on deeper automation, multi-agent orchestration, and enterprise-scale deployments.
“Over the past few years, the challenges of managing multiple disjointed SaaS tools have become evident. Removing this fragmentation needs a new system-of-work and system-of-insights for teams to move faster. Today, it’s possible to run operations on agentic architecture, solving for siloed data. We’ve seen first-hand the impact on business performance and team productivity. SaaS is converging, and a work companion is the right solution,” said Mr. Arun Singh, CEO & Co-Founder, Kris@Work.
“Sales and Revenue teams today are overwhelmed by a proliferation of disconnected point solutions, leading to legacy SaaS fatigue and a measurable decline in productivity,” said Ms. Kitty Agarwal, Partner at InfoEdge Ventures. “As large language models mature, we believe the market is at an inflection point where unified, intelligent platforms with AI co-pilots can replace fragmented workflows. Kris@Work’s vision of becoming a one-stop operating system for Revenue teams, automating repetitive work and enabling teams to focus on higher-value, creative outcomes, is the need of the hour. The team’s strong AI-first product approach positions them well to lead the next generation of integrated AI-first platforms for enterprises”
“SaaS is at a new inflection point. Agentic architectures and context-aware AI are making platforms smarter and more unified. What once required multiple tools can now be achieved through a single platform, faster and with better results,” said Mr. Ramakrishna Mallya, CTO & Co-Founder, Kris@Work.
“A single platform is only half the solution. True outcomes come from pairing it with an intuitive user experience and real-time AI intelligence. Converging everything at work into a single intelligent window is how we see the future of work,” said Mr. Ananta Joshi, Chief Product Officer, Kris@Work.
Powered by a combination of agentic architecture and contextual AI, Kris delivers a single platform for GTM teams, unifying lead identification to deal close to account expansion with contextual AI that drives real-time insights & guidance, automating every step, while keeping governance tight.
The platform is built on a modular, agentic architecture that is designed to extend beyond GTM use cases. Over the next three years, Kris@Work plans to expand this execution layer to additional enterprise functions, applying the same AI-native framework across multiple areas of work.
The platform is currently being adopted across industries including Technology, Financial Services, Telecom, and Automotive, where complex GTM motions and fragmented tool stacks continue to be a major challenge.
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