Sequoia-incubated Empirik launches with $21M to predict outages before they happen

| Source: TechCrunch AI

Tags: Empirik, Sequoia Capital, AIOps, observability, SRE, DevOps, seed funding, infrastructure

Empirik exits Sequoia's incubator with $21M in seed funding to bring AI-powered outage prediction to infrastructure teams — the platform tracks system changes and infers their ripple effects before failures occur, already counting S&P Global and Guardant Health among its early Fortune 500 customers.

Details

Empirik, incubated inside Sequoia Capital since 2023, is spinning out as an independent company with $21 million in seed funding from Sequoia, Canapi, and Alumni Ventures. The startup was co-founded by Avon Puri and Sudheer Dhurjati — both Sequoia IT leaders with backgrounds at Rubrik, VMware, and other infrastructure-heavy companies — who saw that large language models could help teams predict infrastructure failures before they cascade into outages. The platform works by continuously tracking changes across the entire IT environment and inferring potential ripple effects. It acts as an autonomous traffic cop: automatically approving low-risk changes, applying guardrails to riskier ones, and escalating the most dangerous updates for human review. The practical aim is to free DevOps and SRE teams from routine failure triage so they can focus on higher-priority engineering work. CEO Kartik Chandrayana — former Quantum Metric CPO and Salesforce observability VP — frames the ambition plainly: 'What agentic AI did for software, Empirik wants to do for infrastructure engineering.' That pitch has already landed real enterprise contracts: named customers include S&P Global, Guardant Health, and at least one other Fortune 500 company in CPG. Sequoia partner Bogomil Balkansky positions Empirik as a complementary layer to AI SRE platforms like Resolve and portfolio company Traversal, not a replacement. The timing reflects a real pressure point: as AI accelerates software shipping velocity, infrastructure teams face more changes per day with the same headcount — and more potential failure points.