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Selector AI Raises $33M in Series B Funding

Selector announces the close of its $33M Series B funding round.

The round was led by Ansa Capital, a New York City-based venture capital firm that invests in enterprise software companies at the forefront of new markets, systems, and optimized distribution models. It was joined by existing investors Two Bear Capital, Atlantic Bridge, and Sinewave Ventures, as well as AT&T Ventures, Bell Ventures, Singtel Innov8, and Hyperlink Ventures. The new investment brings Selector’s total funding to more than $66 million.

Founded by Juniper Networks executives Kannan Kothandaraman and Nitin Kumar, Selector’s team is composed of network, machine learning and Large Language Model (LLM) experts from companies including Juniper, Uber, Meta, Cisco, Nutanix and VMWare. The company’s technology is already deployed by some of the largest telecommunications and enterprise companies and is seeing increased adoption by new large-scale customers. Selector will use the funding to accelerate the development of its AIOps, LLM and Digital Twin technologies, enabling unprecedented end-to-end network and infrastructure visibility for companies. It will additionally expand its geographic footprint with new offices and staff across the US, Canada, Europe, Singapore, India, and Japan.

Selector's AI engine directly interfaces with a network language model platform so that teams can have real-time conversations with their full-stack data in human language to determine precisely where issues originated.

“Even the slightest performance degradation or downtime is unacceptable for mission-critical infrastructure and services. Solving this problem requires auto-correlating across enormous volumes of data. That’s why we exist,” said Selector CEO Kannan Kothandaraman. “We’ve spent the last five years demonstrating how autonomous AI technology and human network expertise can work together to ensure that the world’s most demanding networks are up, operating, and generating revenue at all times. We’re now ready to scale this work significantly.”

Selector’s investors, which now include major telecommunications companies that are directly benefiting from its solution, see the expansive potential that solving this problem will create–both for users and the technology company itself.

“Selector is in a position to solve a very expensive problem for very large companies,” said Allan Jean-Baptiste, Co-founder and General Partner at Ansa Capital, and Selector board member. “The rise of cloud technology, distributed microservices, and the need for seamless 24/7 performance have significantly increased the demands on enterprise network teams. No other tools on the market can watch their entire corporate infrastructure. Selector, on the other hand, has proven that it can achieve 360-degree visibility across networks–and prescribe action instantly after an incident occurs. This creates a massive opportunity for them as networks rise in importance to enterprises around the world.”

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Selector AI Raises $33M in Series B Funding

Selector announces the close of its $33M Series B funding round.

The round was led by Ansa Capital, a New York City-based venture capital firm that invests in enterprise software companies at the forefront of new markets, systems, and optimized distribution models. It was joined by existing investors Two Bear Capital, Atlantic Bridge, and Sinewave Ventures, as well as AT&T Ventures, Bell Ventures, Singtel Innov8, and Hyperlink Ventures. The new investment brings Selector’s total funding to more than $66 million.

Founded by Juniper Networks executives Kannan Kothandaraman and Nitin Kumar, Selector’s team is composed of network, machine learning and Large Language Model (LLM) experts from companies including Juniper, Uber, Meta, Cisco, Nutanix and VMWare. The company’s technology is already deployed by some of the largest telecommunications and enterprise companies and is seeing increased adoption by new large-scale customers. Selector will use the funding to accelerate the development of its AIOps, LLM and Digital Twin technologies, enabling unprecedented end-to-end network and infrastructure visibility for companies. It will additionally expand its geographic footprint with new offices and staff across the US, Canada, Europe, Singapore, India, and Japan.

Selector's AI engine directly interfaces with a network language model platform so that teams can have real-time conversations with their full-stack data in human language to determine precisely where issues originated.

“Even the slightest performance degradation or downtime is unacceptable for mission-critical infrastructure and services. Solving this problem requires auto-correlating across enormous volumes of data. That’s why we exist,” said Selector CEO Kannan Kothandaraman. “We’ve spent the last five years demonstrating how autonomous AI technology and human network expertise can work together to ensure that the world’s most demanding networks are up, operating, and generating revenue at all times. We’re now ready to scale this work significantly.”

Selector’s investors, which now include major telecommunications companies that are directly benefiting from its solution, see the expansive potential that solving this problem will create–both for users and the technology company itself.

“Selector is in a position to solve a very expensive problem for very large companies,” said Allan Jean-Baptiste, Co-founder and General Partner at Ansa Capital, and Selector board member. “The rise of cloud technology, distributed microservices, and the need for seamless 24/7 performance have significantly increased the demands on enterprise network teams. No other tools on the market can watch their entire corporate infrastructure. Selector, on the other hand, has proven that it can achieve 360-degree visibility across networks–and prescribe action instantly after an incident occurs. This creates a massive opportunity for them as networks rise in importance to enterprises around the world.”

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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