Empirik Emerges from Stealth with $21M to Build Autonomous Infrastructure Engineering Platform

Empirik Emerges from Stealth with $21M to Build Autonomous Infrastructure Engineering Platform Empirik, an infrastructure startup originally incubated inside Sequoia, has emerged from stealth with $21 million in funding. Led by CEO Kartik Chandrayana alongside founders Avon Puri and Sudheer Dhurjati, the company aims to move enterprise IT from reactive observability to proactive autonomy by turning infrastructure changes into a real-time, unified operational graph. Empirik emerged from stealth with $21 million in funding under the leadership of CEO Kartik Chandrayana, alongside founders Avon Puri and Sudheer Dhurjati. The platform addresses a core blind spot in traditional observability by focusing directly on the stream of infrastructure changes rather than post-mortem symptoms. Using an infrastructure compiler, Empirik integrates metadata across public clouds, identity providers, CI/CD, and SaaS systems to generate a live operational graph. Early enterprise customers include Guardant Health, TCBPay, Avahi, and unnamed Fortune 50 and Fortune 500 companies, which use it to detect, evaluate, and block risky changes. From Incubation to Independent Startup Empirik originated inside Sequoia from insights developed by Chief Digital and Information Officer Avon Puri and Sudheer Dhurjati, both veterans of VMware and Rubrik. Observing that traditional observability tools operate post-mortem on symptoms rather than causes, they developed the initial technology inside Sequoia before spinning it out as an independent company. It is now led by repeat founder Kartik Chandrayana, who previously held senior product roles at Salesforce and Quantum Metric. Focusing on Change Over Symptoms While traditional observability focuses on metrics like latency spikes or crashed pods, Empirik positions infrastructure change—such as modified identity policies or deployed Terraform providers—as the foundational pillar of operational behavior. By evaluating changes at inception, the platform aims to compute risk, identify who made modifications, and block problematic deployments before they reach production. The Infrastructure Compiler and Traction Empirik functions as an infrastructure compiler that maps dependencies across hybrid IT environments, including legacy on-prem infrastructure, multiple public clouds, and SaaS applications. The system plugs into agentic workflows through MCP to interface with models like Claude and ChatGPT. Early deployments show TCBPay reducing sensitive config change detection time from 30 minutes to 10 seconds, while Avahi uses it inside GitHub Actions to evaluate and block risky updates.

红杉资本支持的自主基础设施工程平台 Empirik 走出隐形状态并获 2100 万美元融资 红杉资本宣布投资由 Avon Puri 和 Sudheer Dhurjati 孵化、Kartik Chandrayana 担任 CEO 的初创公司 Empirik。该公司正式走出隐形状态并筹集了 2100 万美元资金，旨在将企业基础设施管理从被动的指标观测转向主动的变更驱动与自主工程代理。 Empirik 从红杉资本内部孵化后分拆独立，完成 2100 万美元融资，由连续创业者 Kartik Chandrayana 出任 CEO。 传统可观测性工具侧重于事后衡量指标症状，而 Empirik 将“变更”作为核心要素，在事前评估风险并阻止危险部署。 平台通过“基础设施编译器”将多云、混合云及身份系统等元数据转化为统一的操作图谱，并通过 MCP 协议接入 Claude、ChatGPT 等 AI 工具。 客户覆盖多家大型企业，每周处理数百万次变更与遥测事件，图谱涵盖跨多云和本地环境的数百万个资源。 从被动可观测转向变更驱动 传统监控与可观测性工具如 Datadog、Dynatrace 和 Splunk 主要测量系统故障的表象症状，往往属于事后分析。Empirik 认为基础设施行为的真正驱动力是变更。通过实时理解从代码、Terraform 到身份策略的每一次变更意图，系统可以在变更执行前计算风险，甚至直接阻止高危操作，使基础设施管理从被动排障转向主动预防。 构建基础设施编译器与操作图谱 由于企业基础设施缺乏统一的版本控制图谱，Empirik 打造了基础设施编译器。它从客户的公有云、身份提供商、CI/CD 及 SaaS 系统中提取元数据，生成包含数千种资产与账户关系的实时统一操作图谱。该方案针对财富 500 强的复杂混合栈设计，并通过 MCP 协议与 Claude 和 ChatGPT 等大模型工具连接，为 AI 自主代理与工程师提供完整的上下文。 落地实践与企业客户反馈 Empirik 目前每周处理数百万次原始变更及遥测事件，覆盖数百万个基础设施资源。客户案例显示，支付处理机构 TCBPay 将敏感配置文件变更的捕获时间从 30 分钟缩短至 10 秒；托管服务商 Avahi 将其集成至 GitHub Actions 以拦截源头风险；Guardant Health 及某财富 50 强消费品企业也扩大了部署，用于防范未批准变更、加速事故响应及配置漂移检测。

This one started close to home. Before Sequoia, our Chief Digital and Information Officer, Avon Puri , had spent two decades running IT and infrastructure at scale as SVP & CIO at Rubrik, and VP of Engineering and Applications at VMware (also my alma mater in the tech world). Avon and Sudheer Dhurjati , who worked alongside him at all three companies, watched the same story repeat: enterprise infrastructure operates in a reactive posture. Companies spend tens of billions of dollars on observability—metrics, logs, and traces—to tell us when a system is failing. But traditional observability is inherently post-mortem. The real driver of infrastructure behavior is change: a new identity policy is attached, a new Terraform provider completes, or a feature flag flips in production. However, change has been treated as an administrative process constraint, a static ticket, an approval form, or as tribal knowledge buried in email or Slack. It was sometimes recorded for compliance, but never truly put to work to do actual infrastructure engineering jobs. If you understand the real-time stream of changes across every layer of the environment, you don't just get better alerts—you unlock true autonomy. Every change reflects an engineer's intent; once a system can interpret that intent and assess its impact, it can decide whether to act on its own or loop in a human.  By bringing agentic capabilities to infrastructure, you solve the full lifecycle of infrastructure engineering jobs, shifting from a reactive firefighting posture to a proactive one. That insight became the founding pillar behind Empirik . Avon and Sudheer built the initial version inside Sequoia. We saw companies rushing to deploy AI agents to automate coding, marketing, and many other enterprise processes, but infrastructure was the one place nobody was modernizing. After the same rigorous evaluation any other Sequoia investment goes through, Empirik spun out as an independent company. And now, it’s under the leadership of CEO Kartik Chandrayana , a repeat founder who sold his first company, Twin Prime, to Salesforce, then spent five years as Salesforce's VP of Product for Observability and Big Data, and most recently was CPO at Quantum Metric. Today, we're excited to share that Empirik is emerging from stealth with $21M in funding. Why change, and why now Agentic coding has taken off because code lives in version-controlled repositories that make a system's architecture and intent legible to a model. Infrastructure has never had that foundation. Infrastructure-as-code is a good start, but because it represents a declared state and not what's actually running in production, it's inherently behind. There's no unified, real-time representation of an organization's infrastructure across the maze of cloud resources, Kubernetes, networking, identity, and legacy systems. Infrastructure does not have the notion of a pull request; instead, every change still runs through tribal knowledge and institutional memory of people rather than the machine rigor of software. Observability is already a large, proven market—Datadog, Dynatrace, and Splunk are behemoths with billions in revenue. But all of observability is built to measure symptoms: latency spiked, errors climbed, a pod crashed. Empirik's bet is that there's a fourth pillar hiding in plain sight—the changes themselves. Instead of "latency in service X increased 300% in the last 24 hours," Empirik tells you, "Joe deployed a Terraform provider that shut down three VMs in AWS"—the actual cause and context, not just the symptom. And why stop there? With Empirik, you can actually warn or stop Joe even before he deploys Terraform because Empirik computes that risk at the inception of change. Building the infrastructure compiler As Kartik describes it: infrastructure today is where software engineering was three years ago—before Claude Code and Cursor, when every action needed a human in the loop because there was no reliable map of the system to reason over. Empirik is building that map, and putting it to work as the Autonomous Infra Engineer—AI that can safely plan, reason about, and operate enterprise infrastructure. It's already plugged into agentic workflows via MCP, connecting directly into tools like Claude and ChatGPT, so that humans and agents alike can reason over infrastructure change with real context—not guesswork. Under the hood is an infrastructure compiler. Just as a traditional compiler translates source code into executable software, Empirik converts metadata from a customer’s clouds, identity providers, CI/CD, and SaaS systems into a live, unified operational graph—mapping thousands of relationships between assets and accounts. That graph is how the product knows, in real time, what changed, who changed it, and whether it’s the reason something broke. What I particularly appreciate about the team’s approach is that they didn’t take shortcuts. It’s easy for modern startups to build exclusively for greenfield Kubernetes or a single public cloud provider. But from day one, Avon, Sudheer, and Kartik designed Empirik for the reality of the Fortune 500 enterprise: a complex, multi-decade hybrid stack spanning public clouds, legacy on-prem infrastructure, identity layers, and critical SaaS apps. An infrastructure graph that only sees half your environment misses the mark for an enterprise buyer. Empirik’s graph connects the entire surface area so teams and agents have complete operational context. The critical question, answered The traction backs up the thesis. Fortune500 Financial enterprise’s interest in the product traces directly back to a P0 outage caused by an unapproved change. TCBPay, a payment processing company with $1.5B+ in annual volume now catches sensitive config file changes in 10 seconds, down from 30 minutes. Avahi, an MSP, integrated Empirik within their GitHub Actions to evaluate and block risky changes at the source. Guardant Health found Empirik so valuable that it expanded its deployment from production to all dev environments, and even expanded to on-prem. And a global Fortune-50 CPG company is also expanding its footprint, relying on Empirik to drive most of its agentic and autonomous infra roadmap. Empirik is now processing millions of raw change and telemetry events every week, over dependency graphs spanning a few million resources across all major clouds and on-prem systems. Customers are reporting benefits in multiple domains: preventing risky changes from making it to production, detecting unapproved changes, faster incident response, making rollback decisions, and detecting drifts, among others. The success is no surprise given this team. Avon and Sudheer are two of the best operators we know, and they built Empirik the way they'd have wanted it built for themselves. Kartik, meanwhile, has firsthand expertise in shipping observability products at enterprise scale. Most enterprises still can't answer that simple but critical question: what changed, and did that cause the incident? Empirik is building the system of record for that question—for humans today, and for the agents that will increasingly be making those changes themselves tomorrow.