Netomi is an enterprise AI company that develops agentic customer experience software for large organizations. Its platform uses AI agents to understand customer context, reason across business rules and data, and take actions across chat, email, voice and other digital channels. Netomi is used by brands including Delta Air Lines, United Airlines, MetLife, Paramount, DraftKings and the NBA. Its Agentic AI architecture is designed for complex, high-volume and regulated environments, combining automated customer resolution with governance, tool orchestration and integrations into enterprise systems. The company works with AI models from providers including OpenAI and focuses on deploying production-grade autonomous customer experiences at enterprise scale.
Netomi offers candidates the opportunity to build production-grade agentic AI used by major global enterprises including Delta Air Lines, United Airlines, MetLife, Paramount and DraftKings. The company raised $110 million in Series C funding in 2026, bringing total funding above $160 million, and has strategic relationships with Accenture and Adobe. Its engineering work includes large-scale AI orchestration, governed tool use, distributed infrastructure and high-volume customer interactions; an OpenAI case study reported workloads above 40,000 concurrent requests per second. Current roles span agentic AI, data science, frontend, data engineering and cloud infrastructure, with technologies including OpenAI models, AWS, Kubernetes, Terraform and Python.
AI Model
AI Model
Cloud Infrastructure
Container Orchestration
Container Infrastructure
Infrastructure as Code
CI/CD
Continuous Delivery
Kubernetes Package Management
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Containers
Founder and CEO of Netomi. Before Netomi, Mehta co-founded MyCityWay and MobileROI and held engineering and data science roles at Citi, Merrill Lynch and JPMorgan Chase. He studied Computer Science and Engineering at Punjab Engineering College and has extensive experience building AI and data systems, including algorithmic technology for financial markets.
Leads AI and Agentic Factory deployment at Netomi. He joined in 2026 to help scale production-grade agentic systems and self-evolving AI agents for enterprise customers. His background spans AI, machine learning, data science, engineering leadership and applying advanced analytics to operational problems.
Leads Netomi's global field engineering organization, focused on deploying and scaling agentic AI in enterprise environments. Before Netomi, Owen spent several years at Elastic, where he helped build and lead a Customer Architecture organization supporting enterprise adoption and technical outcomes.
Leads Customer Success at Netomi, focusing on enterprise adoption, measurable customer outcomes and expansion of the company's AI platform within customer organizations. His role connects platform deployment with long-term customer value and successful production use.
Leads Netomi's global alliances, partner and channel strategy. Before joining Netomi, Miller spent more than 18 years at Accenture working at the intersection of technology, business transformation and Fortune 500 strategy. She is responsible for expanding Netomi's ecosystem and enterprise partnerships.
Business Wire
Netomi raised $110 million in Series C funding led by Accenture Ventures, with participation from Adobe Ventures, WndrCo, Silver Lake Waterman, NAVER Ventures, Metis Strategy and Fin Capital. The financing brought total funding to more than $160 million and is intended to support R&D and expansion of enterprise deployments.
Read articleAdobe
Adobe announced Netomi as a partner for Adobe Brand Concierge. The integration is intended to connect Adobe and Netomi agents to deliver governed agentic AI experiences spanning product discovery, search, customer support and loyalty workflows.
Read articleOpenAI
OpenAI published a case study detailing how Netomi uses GPT-4.1 and GPT-5.2 in its Agentic OS to support multi-step enterprise workflows, governance and high-volume customer interactions. The case study reported sub-three-second responses and 98% intent classification accuracy during high-traffic workloads.
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