Bengaluru, Karnataka, India
AI Product Manager with 5+ years building at the intersection of AI, enterprise software, and product strategy. Currently at GreyLabs as an AI Product Manager, I'm focused on building a platform where we can deploy millions of voice agents to transform the BFSI industry. At Moveworks (acquired by ServiceNow), I shipped conversational AI data agents, launched 50+ AI agents on the marketplace, and transformed a code-heavy platform into a low-code builder — cutting agent build time by 20% and driving $1M+ in partner-led deals. I also wrote the book "The AI Stack — From Silicon to Systems", a field guide breaking down the entire AI stack from transistors to agents.
Building the next generation of voice-first banking agents.
[1] MW analytics and data products - Enabling executives to visualise the performance of their AI assistant and custom AI agents. - Supporting Economic buyers and MW champions to calculate their own dollar value and employee time saved because of AI assistant via their own value calculation framework and performance metrics. - Empowering analysts to spot opportunity areas for increasing employee productivity via AI assistant I am currently owning the entire roadmap and leading a team of 10 people. [2] AI Agent builder: The future of everything are AI agents, lowering the bar to create one is my goal. The problem is , how do you enable numerous system with their own API data model, to converse with each other ? How do you enable an agent to orchestrate conversation between numerous systems ? In simple words..How do you ensure that ONE single screwdriver is compatible with every single screw out there. I am currently working on the Agent builder core module, targeting at-least 50% reduction in time to create plus deploy an agent & ensuring that “Everyone can build an AI agent” [3] Content auditor How many times have you created a document , and instead of manually sharing with 10 people , you just clicked on “Share anyone with the link” ? To make Enterprises AI ready, detecting and fixing overshared , outdated content is crucial and that’s what this product is aimed to do. Currently working on this product , targeting “Agentic AI readiness report in 30 minutes ” As MW is acquired by ServiceNow, we look to deploy MW to atleast 5000 more customers (currently around 300 customers) This product lowers the barrier to adopt Agentic AI for any enterprise.
[1] MW analytics and data products - Delivered over 10 analytics dashboards spanning across various AI assistant verticals. - Delivered Data API for developers to develop custom dashboards in their BI tools. - Orchestrated a governance layer on all data products [RBAC and ReBAC] Impact : 1. MW champions could self-serve all their data requirements , removing their dependancy on Customer success almost entirely. 2. Driving adoption of other products [MW Setup, Enterprise search, Agent builder ] 3. Better analytics > More usage > More retention + upsell. [2] MW Setup : An admin tool to deploy, manage and troubleshoot your AI assistant. MW did not have a self-serve tool , we had to conceptualise all AI assistant capabilities and bucket them under controls, it was like designing all driver controls for a car. The car being enterprise wide AI assistant and the driver being a MW AI assistant admin. Delivered a product supporting 100+ use cases streamlined into 5 cohesive modules. Impact : 1. Unlocked Professional services margin for MW [entirely new revenue stream ] 2. Unlocked Partner ecosystem [Onboarded and enabled 2 partners - Deloitte was one] 3. Drove down customer success cost (~41%)
[1] Integrations Responsible for natively integrating MW platform with various ITSM, IDAM and KM systems while focusing on core agentic AI use cases. Integrated with over 10 systems in 3 months [Inception to delivery] Impact : 3 new customers, cumulative ARR > 500K
1) Developing proof of concept followed by prototypes for new product features driven by Data and Product team. 2) Doing product research - AWS and Azure for identification of new actions for the MCMP( Multi cloud management platform ). 3) Test driven development for the core engine and the integration adapter of the product leveraging cutting edge technologies.
Multi Cloud Management Platform (MCMP) 1) Developing logical solutions for the backend of the platform. 2) Designing playbooks to automate tasks on all major cloud providers- Azure, AWS, IBM cloud, Alibaba Cloud etc. for the end users. 3) Developing and deploying customized modules that simplify complex processes over all major cloud providers.
Leading a team to promote innovation and to enforce regulations as instructed by the Ministry of Human Resource Development (MHRD), Govt. of India in VIT, Vellore campus.